{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- By: Alex Kwon\n",
    "- Email: alex.kwon [at] hudsonthames [dot] org\n",
    "\n",
    "# Online Portfolio Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OLPS Strategies\n",
    "\n",
    "[**Benchmarks**](https://github.com/hudson-and-thames/research/blob/master/Online%20Portfolio%20Selection/Introduction%20to%20Online%20Portfolio%20Selection.ipynb)\n",
    "\n",
    "[**Momentum**](https://github.com/hudson-and-thames/research/blob/master/Online%20Portfolio%20Selection/Online%20Portfolio%20Selection%20-%20Momentum.ipynb)\n",
    "\n",
    "[**Mean Reversion**](https://github.com/hudson-and-thames/research/blob/master/Online%20Portfolio%20Selection/Online%20Portfolio%20Selection%20-%20Mean%20Reversion.ipynb)\n",
    "\n",
    "[**Pattern Matching**](https://github.com/hudson-and-thames/research/blob/master/Online%20Portfolio%20Selection/Online%20Portfolio%20Selection%20-%20Pattern%20Matching.ipynb)\n",
    "\n",
    "## Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Online Portfolio Selection is an algorithmic trading strategy that sequentially allocates capital among a group of assets to maximize the final returns of the investment.\n",
    "\n",
    "Traditional theories for portfolio selection, such as Markowitz’s Modern Portfolio Theory, optimize the balance between the portfolio's risks and returns. However, OLPS is founded on the capital growth theory, which solely focuses on maximizing the returns of the current portfolio.\n",
    "\n",
    "Through these walkthroughs of different portfolio selection strategies, we hope to introduce a set of different selection tools available for everyone. Most of the works will be based on Dr. Bin Li and Dr. Steven Hoi’s book, *Online Portfolio Selection: Principles and Algorithms*, and further recent papers will be implemented to assist the development and understanding of these unique portfolio selection strategies.\n",
    "\n",
    "The package and module behind this implementation of OLPS are currently in the works and will be published on MlFinLab soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important Notes\n",
    "\n",
    "We will primarily focus on two ideas for the implementation of these strategies.\n",
    "\n",
    "1. Online Learning\n",
    "    - Algorithms should be optimized to backtest years of data without any problems\n",
    "2. Intuitive user experience\n",
    "    - Simple methods that will allow the users to easily interact with the data of their choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mlfinlab.online_portfolio_selection.benchmarks import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the ETF data included in the MlFinLab library for analysis. This includes 23 ETF's with closing prices from 2008 to 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_prices = pd.read_csv('../../mlfinlab/mlfinlab/tests/test_data/stock_prices.csv', parse_dates=True, index_col='Date')\n",
    "stock_prices = stock_prices.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "      <th>2008-01-02</th>\n",
       "      <td>49.273335</td>\n",
       "      <td>35.389999</td>\n",
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       "      <td>52.919998</td>\n",
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       "      <td>47.759998</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.090000</td>\n",
       "      <td>51.173328</td>\n",
       "      <td>55.983330</td>\n",
       "      <td>74.529999</td>\n",
       "      <td>67.309998</td>\n",
       "      <td>144.929993</td>\n",
       "      <td>94.379997</td>\n",
       "      <td>77.360001</td>\n",
       "      <td>101.400002</td>\n",
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       "      <th>2008-01-03</th>\n",
       "      <td>49.716667</td>\n",
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       "      <td>37.919998</td>\n",
       "      <td>48.060001</td>\n",
       "      <td>42.049999</td>\n",
       "      <td>80.440002</td>\n",
       "      <td>...</td>\n",
       "      <td>42.029999</td>\n",
       "      <td>51.293331</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>74.800003</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>144.860001</td>\n",
       "      <td>94.250000</td>\n",
       "      <td>77.459999</td>\n",
       "      <td>101.519997</td>\n",
       "      <td>130.740005</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>48.223331</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>106.970001</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>76.570000</td>\n",
       "      <td>88.040001</td>\n",
       "      <td>36.990002</td>\n",
       "      <td>46.919998</td>\n",
       "      <td>40.779999</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.349998</td>\n",
       "      <td>49.849998</td>\n",
       "      <td>54.536671</td>\n",
       "      <td>72.980003</td>\n",
       "      <td>65.769997</td>\n",
       "      <td>141.309998</td>\n",
       "      <td>94.269997</td>\n",
       "      <td>77.550003</td>\n",
       "      <td>101.650002</td>\n",
       "      <td>128.169998</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>48.576668</td>\n",
       "      <td>34.630001</td>\n",
       "      <td>106.949997</td>\n",
       "      <td>51.439999</td>\n",
       "      <td>76.650002</td>\n",
       "      <td>88.199997</td>\n",
       "      <td>37.259998</td>\n",
       "      <td>47.060001</td>\n",
       "      <td>40.220001</td>\n",
       "      <td>77.199997</td>\n",
       "      <td>...</td>\n",
       "      <td>43.230000</td>\n",
       "      <td>50.416672</td>\n",
       "      <td>56.116669</td>\n",
       "      <td>72.949997</td>\n",
       "      <td>65.650002</td>\n",
       "      <td>141.190002</td>\n",
       "      <td>94.680000</td>\n",
       "      <td>77.570000</td>\n",
       "      <td>101.720001</td>\n",
       "      <td>128.059998</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>48.200001</td>\n",
       "      <td>34.389999</td>\n",
       "      <td>107.029999</td>\n",
       "      <td>51.320000</td>\n",
       "      <td>76.220001</td>\n",
       "      <td>88.389999</td>\n",
       "      <td>36.970001</td>\n",
       "      <td>46.400002</td>\n",
       "      <td>39.599998</td>\n",
       "      <td>75.849998</td>\n",
       "      <td>...</td>\n",
       "      <td>43.240002</td>\n",
       "      <td>49.566669</td>\n",
       "      <td>55.326672</td>\n",
       "      <td>72.400002</td>\n",
       "      <td>65.360001</td>\n",
       "      <td>138.910004</td>\n",
       "      <td>94.570000</td>\n",
       "      <td>77.650002</td>\n",
       "      <td>101.739998</td>\n",
       "      <td>125.849998</td>\n",
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      "text/plain": [
       "                  EEM        EWG         TIP        EWJ        EFA        IEF  \\\n",
       "Date                                                                            \n",
       "2008-01-02  49.273335  35.389999  106.639999  52.919998  78.220001  87.629997   \n",
       "2008-01-03  49.716667  35.290001  107.000000  53.119999  78.349998  87.809998   \n",
       "2008-01-04  48.223331  34.599998  106.970001  51.759998  76.570000  88.040001   \n",
       "2008-01-07  48.576668  34.630001  106.949997  51.439999  76.650002  88.199997   \n",
       "2008-01-08  48.200001  34.389999  107.029999  51.320000  76.220001  88.389999   \n",
       "\n",
       "                  EWQ        EWU        XLB        XLE  ...        XLU  \\\n",
       "Date                                                    ...              \n",
       "2008-01-02  37.939999  47.759998  41.299999  79.500000  ...  42.090000   \n",
       "2008-01-03  37.919998  48.060001  42.049999  80.440002  ...  42.029999   \n",
       "2008-01-04  36.990002  46.919998  40.779999  77.500000  ...  42.349998   \n",
       "2008-01-07  37.259998  47.060001  40.220001  77.199997  ...  43.230000   \n",
       "2008-01-08  36.970001  46.400002  39.599998  75.849998  ...  43.240002   \n",
       "\n",
       "                  EPP        FXI        VGK        VPL         SPY        TLT  \\\n",
       "Date                                                                            \n",
       "2008-01-02  51.173328  55.983330  74.529999  67.309998  144.929993  94.379997   \n",
       "2008-01-03  51.293331  55.599998  74.800003  67.500000  144.860001  94.250000   \n",
       "2008-01-04  49.849998  54.536671  72.980003  65.769997  141.309998  94.269997   \n",
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       "2008-01-08  49.566669  55.326672  72.400002  65.360001  138.910004  94.570000   \n",
       "\n",
       "                  BND         CSJ         DIA  \n",
       "Date                                           \n",
       "2008-01-02  77.360001  101.400002  130.630005  \n",
       "2008-01-03  77.459999  101.519997  130.740005  \n",
       "2008-01-04  77.550003  101.650002  128.169998  \n",
       "2008-01-07  77.570000  101.720001  128.059998  \n",
       "2008-01-08  77.650002  101.739998  125.849998  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Formulation\n",
    "\n",
    "### Price: $p_t$\n",
    "\n",
    "The price of the data is inputted by the user. Asset $i$'s price at time $t$ will be referred to as $p_{t,i}$.\n",
    "\n",
    "### Price Relative: $x_t = \\frac{p_t}{p_{t-1}}$\n",
    "\n",
    "The price relative is calculated by taking the ratio of the current time's price to the last time's price.\n",
    "\n",
    "Asset $i$'s price relative at time $t$ will be referred to as $x_{t,i}$.\n",
    "\n",
    "### Portfolio Weight: $b_t$\n",
    "\n",
    "The portfolio weights represent the allocation of capital to a particular asset.\n",
    "\n",
    "We will assume that the weights are non-negative and that sum of the weight is one, which will simulate a long-only, no leverage environment.\n",
    "\n",
    "### Portfolio Return: $S_t = S_0\\overset{t}{\\underset{i=0}{\\prod}} x_i \\cdot b_i$\n",
    "\n",
    "The cumulative portfolio returns will be calculated by taking the product of all previous returns.\n",
    "\n",
    "$S_0$ represents the initial capital, and each dot product of price relative and portfolio weights represent the increase in capital for a particular time period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarks\n",
    "\n",
    "Before we dive into the more interesting and complex models of portfolio selection, we will begin our analysis with benchmarks. As unappealing as benchmarks are, traditional strategies such as tracking the S&P 500 have been hugely successful.\n",
    "\n",
    "Typically these are implemented in hindsight, so future data is often incorporated within the selection algorithm. For real-life applications, we do not have access to future data from the present, so strategies here should be taken with a grain of salt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Buy and Hold\n",
    "\n",
    "Buy and Hold is a strategy where an investor invests in an initial portfolio and never rebalances it. The portfolio weights, however, change as time goes by because the underlying assets change in prices.\n",
    "\n",
    "Returns for Buy and Hold can be calculated by multiplying the initial weight and the cumulative product of relative returns.\n",
    "\n",
    "$S_n(BAH(b_1)) = b_1 \\cdot \\left(\\overset{n}{\\underset{t=1}{\\bigodot}} x_t\\right)$\n",
    "\n",
    "Buy and Hold strategy can be called using **BAH()**.\n",
    "\n",
    "We can then easily run the algorithm and allocate weights by using the **.allocate()** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bah = BAH()\n",
    "bah.allocate(stock_prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we didn't specify a weight when we allocated to the strategy, initial weights are uniformly distributed across all the ETF's for time 0.\n",
    "\n",
    "The weights allocated to this portfolio can be viewed by using **.all_weights** on our portfolio object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>2008-01-02</th>\n",
       "      <td>0.043478</td>\n",
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       "      <td>0.043478</td>\n",
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       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>0.043781</td>\n",
       "      <td>0.043268</td>\n",
       "      <td>0.043537</td>\n",
       "      <td>0.043554</td>\n",
       "      <td>0.043462</td>\n",
       "      <td>0.043479</td>\n",
       "      <td>0.043367</td>\n",
       "      <td>0.043663</td>\n",
       "      <td>0.044178</td>\n",
       "      <td>0.043903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043328</td>\n",
       "      <td>0.043492</td>\n",
       "      <td>0.043093</td>\n",
       "      <td>0.043547</td>\n",
       "      <td>0.043513</td>\n",
       "      <td>0.043369</td>\n",
       "      <td>0.043331</td>\n",
       "      <td>0.043446</td>\n",
       "      <td>0.043442</td>\n",
       "      <td>0.043427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>0.043220</td>\n",
       "      <td>0.043175</td>\n",
       "      <td>0.044298</td>\n",
       "      <td>0.043193</td>\n",
       "      <td>0.043230</td>\n",
       "      <td>0.044368</td>\n",
       "      <td>0.043055</td>\n",
       "      <td>0.043384</td>\n",
       "      <td>0.043605</td>\n",
       "      <td>0.043050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044434</td>\n",
       "      <td>0.043019</td>\n",
       "      <td>0.043020</td>\n",
       "      <td>0.043243</td>\n",
       "      <td>0.043151</td>\n",
       "      <td>0.043058</td>\n",
       "      <td>0.044110</td>\n",
       "      <td>0.044270</td>\n",
       "      <td>0.044270</td>\n",
       "      <td>0.043330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>0.043437</td>\n",
       "      <td>0.043114</td>\n",
       "      <td>0.044188</td>\n",
       "      <td>0.042828</td>\n",
       "      <td>0.043176</td>\n",
       "      <td>0.044347</td>\n",
       "      <td>0.043271</td>\n",
       "      <td>0.043415</td>\n",
       "      <td>0.042908</td>\n",
       "      <td>0.042786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045254</td>\n",
       "      <td>0.043409</td>\n",
       "      <td>0.044165</td>\n",
       "      <td>0.043126</td>\n",
       "      <td>0.042974</td>\n",
       "      <td>0.042923</td>\n",
       "      <td>0.044200</td>\n",
       "      <td>0.044180</td>\n",
       "      <td>0.044199</td>\n",
       "      <td>0.043194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EEM       EWG       TIP       EWJ       EFA       IEF  \\\n",
       "Date                                                                     \n",
       "2008-01-02  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-03  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-04  0.043781  0.043268  0.043537  0.043554  0.043462  0.043479   \n",
       "2008-01-07  0.043220  0.043175  0.044298  0.043193  0.043230  0.044368   \n",
       "2008-01-08  0.043437  0.043114  0.044188  0.042828  0.043176  0.044347   \n",
       "\n",
       "                 EWQ       EWU       XLB       XLE  ...       XLU       EPP  \\\n",
       "Date                                                ...                       \n",
       "2008-01-02  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2008-01-03  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2008-01-04  0.043367  0.043663  0.044178  0.043903  ...  0.043328  0.043492   \n",
       "2008-01-07  0.043055  0.043384  0.043605  0.043050  ...  0.044434  0.043019   \n",
       "2008-01-08  0.043271  0.043415  0.042908  0.042786  ...  0.045254  0.043409   \n",
       "\n",
       "                 FXI       VGK       VPL       SPY       TLT       BND  \\\n",
       "Date                                                                     \n",
       "2008-01-02  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-03  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-04  0.043093  0.043547  0.043513  0.043369  0.043331  0.043446   \n",
       "2008-01-07  0.043020  0.043243  0.043151  0.043058  0.044110  0.044270   \n",
       "2008-01-08  0.044165  0.043126  0.042974  0.042923  0.044200  0.044180   \n",
       "\n",
       "                 CSJ       DIA  \n",
       "Date                            \n",
       "2008-01-02  0.043478  0.043478  \n",
       "2008-01-03  0.043478  0.043478  \n",
       "2008-01-04  0.043442  0.043427  \n",
       "2008-01-07  0.044270  0.043330  \n",
       "2008-01-08  0.044199  0.043194  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bah.all_weights.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that the weights change over time becase the ETF prices fluctuate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
       "      <th>IEF</th>\n",
       "      <th>EWQ</th>\n",
       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>0.029938</td>\n",
       "      <td>0.029988</td>\n",
       "      <td>0.049053</td>\n",
       "      <td>0.038726</td>\n",
       "      <td>0.030994</td>\n",
       "      <td>0.057853</td>\n",
       "      <td>0.026079</td>\n",
       "      <td>0.027566</td>\n",
       "      <td>0.050344</td>\n",
       "      <td>0.037951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054111</td>\n",
       "      <td>0.033364</td>\n",
       "      <td>0.026220</td>\n",
       "      <td>0.026964</td>\n",
       "      <td>0.036475</td>\n",
       "      <td>0.063358</td>\n",
       "      <td>0.064999</td>\n",
       "      <td>0.048883</td>\n",
       "      <td>0.047168</td>\n",
       "      <td>0.060049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>0.029835</td>\n",
       "      <td>0.029540</td>\n",
       "      <td>0.049666</td>\n",
       "      <td>0.038889</td>\n",
       "      <td>0.030689</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>0.025715</td>\n",
       "      <td>0.026721</td>\n",
       "      <td>0.049112</td>\n",
       "      <td>0.037086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055299</td>\n",
       "      <td>0.032979</td>\n",
       "      <td>0.026381</td>\n",
       "      <td>0.026511</td>\n",
       "      <td>0.036470</td>\n",
       "      <td>0.062817</td>\n",
       "      <td>0.067257</td>\n",
       "      <td>0.049597</td>\n",
       "      <td>0.047586</td>\n",
       "      <td>0.059777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>0.030271</td>\n",
       "      <td>0.029711</td>\n",
       "      <td>0.049119</td>\n",
       "      <td>0.038906</td>\n",
       "      <td>0.031040</td>\n",
       "      <td>0.058051</td>\n",
       "      <td>0.026115</td>\n",
       "      <td>0.027466</td>\n",
       "      <td>0.048818</td>\n",
       "      <td>0.037569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054705</td>\n",
       "      <td>0.033338</td>\n",
       "      <td>0.026627</td>\n",
       "      <td>0.026986</td>\n",
       "      <td>0.036686</td>\n",
       "      <td>0.063028</td>\n",
       "      <td>0.066426</td>\n",
       "      <td>0.048940</td>\n",
       "      <td>0.046945</td>\n",
       "      <td>0.059817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.030720</td>\n",
       "      <td>0.029810</td>\n",
       "      <td>0.048588</td>\n",
       "      <td>0.039013</td>\n",
       "      <td>0.031286</td>\n",
       "      <td>0.057224</td>\n",
       "      <td>0.026305</td>\n",
       "      <td>0.027893</td>\n",
       "      <td>0.049138</td>\n",
       "      <td>0.037890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054294</td>\n",
       "      <td>0.033510</td>\n",
       "      <td>0.026832</td>\n",
       "      <td>0.027253</td>\n",
       "      <td>0.036800</td>\n",
       "      <td>0.063446</td>\n",
       "      <td>0.065243</td>\n",
       "      <td>0.048423</td>\n",
       "      <td>0.046487</td>\n",
       "      <td>0.060166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.030727</td>\n",
       "      <td>0.029944</td>\n",
       "      <td>0.048208</td>\n",
       "      <td>0.038302</td>\n",
       "      <td>0.031445</td>\n",
       "      <td>0.056796</td>\n",
       "      <td>0.026561</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>0.049441</td>\n",
       "      <td>0.037823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054931</td>\n",
       "      <td>0.033754</td>\n",
       "      <td>0.026934</td>\n",
       "      <td>0.027586</td>\n",
       "      <td>0.036483</td>\n",
       "      <td>0.063689</td>\n",
       "      <td>0.064849</td>\n",
       "      <td>0.048017</td>\n",
       "      <td>0.046102</td>\n",
       "      <td>0.060380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EEM       EWG       TIP       EWJ       EFA       IEF  \\\n",
       "Date                                                                     \n",
       "2016-06-27  0.029938  0.029988  0.049053  0.038726  0.030994  0.057853   \n",
       "2016-06-28  0.029835  0.029540  0.049666  0.038889  0.030689  0.058910   \n",
       "2016-06-29  0.030271  0.029711  0.049119  0.038906  0.031040  0.058051   \n",
       "2016-06-30  0.030720  0.029810  0.048588  0.039013  0.031286  0.057224   \n",
       "2016-07-01  0.030727  0.029944  0.048208  0.038302  0.031445  0.056796   \n",
       "\n",
       "                 EWQ       EWU       XLB       XLE  ...       XLU       EPP  \\\n",
       "Date                                                ...                       \n",
       "2016-06-27  0.026079  0.027566  0.050344  0.037951  ...  0.054111  0.033364   \n",
       "2016-06-28  0.025715  0.026721  0.049112  0.037086  ...  0.055299  0.032979   \n",
       "2016-06-29  0.026115  0.027466  0.048818  0.037569  ...  0.054705  0.033338   \n",
       "2016-06-30  0.026305  0.027893  0.049138  0.037890  ...  0.054294  0.033510   \n",
       "2016-07-01  0.026561  0.028361  0.049441  0.037823  ...  0.054931  0.033754   \n",
       "\n",
       "                 FXI       VGK       VPL       SPY       TLT       BND  \\\n",
       "Date                                                                     \n",
       "2016-06-27  0.026220  0.026964  0.036475  0.063358  0.064999  0.048883   \n",
       "2016-06-28  0.026381  0.026511  0.036470  0.062817  0.067257  0.049597   \n",
       "2016-06-29  0.026627  0.026986  0.036686  0.063028  0.066426  0.048940   \n",
       "2016-06-30  0.026832  0.027253  0.036800  0.063446  0.065243  0.048423   \n",
       "2016-07-01  0.026934  0.027586  0.036483  0.063689  0.064849  0.048017   \n",
       "\n",
       "                 CSJ       DIA  \n",
       "Date                            \n",
       "2016-06-27  0.047168  0.060049  \n",
       "2016-06-28  0.047586  0.059777  \n",
       "2016-06-29  0.046945  0.059817  \n",
       "2016-06-30  0.046487  0.060166  \n",
       "2016-07-01  0.046102  0.060380  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bah.all_weights.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cumulative returns for the portfolio can be called using **.portfolio_return**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
       "      <td>1.002028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>0.984536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>0.986788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>0.977662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>0.953233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>0.967172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>0.977161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.986712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.988987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns\n",
       "Date                \n",
       "2008-01-02  1.000000\n",
       "2008-01-03  1.002028\n",
       "2008-01-04  0.984536\n",
       "2008-01-07  0.986788\n",
       "2008-01-08  0.977662\n",
       "...              ...\n",
       "2016-06-27  0.953233\n",
       "2016-06-28  0.967172\n",
       "2016-06-29  0.977161\n",
       "2016-06-30  0.986712\n",
       "2016-07-01  0.988987\n",
       "\n",
       "[2141 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bah.portfolio_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bah.portfolio_return.plot(title='Buy and Hold',figsize=(18,8)).set_ylabel('Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the crash in 2009, Buy and Hold strategies have returned almost the same returns from the initial allocation. The results would vary depending on the initial weights of stock that we would have used, but initial analysis that uses uniform weights indicates that Buy and Hold has been an unprofitable portfolio for this set of assets.\n",
    "\n",
    "In the event that we would like to allocate a predetermined set of weights to a portfolio, we can initialize the weights as well. For this, we will allocate 0.5 to the first asset and 0.5 to the second asset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,\n",
       "       0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_and_half = np.zeros(stock_prices.shape[1])\n",
    "half_and_half[[0,1]] = 0.5\n",
    "half_and_half"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "half_and_half_bah = BAH()\n",
    "half_and_half_bah.allocate(stock_prices, weights=half_and_half)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
       "      <th>IEF</th>\n",
       "      <th>EWQ</th>\n",
       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>0.500000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>0.502947</td>\n",
       "      <td>0.497053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>0.500259</td>\n",
       "      <td>0.499741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
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       "      <td>0.498133</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>0.499579</td>\n",
       "      <td>0.500421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>0.502483</td>\n",
       "      <td>0.497517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>0.504671</td>\n",
       "      <td>0.495329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.507524</td>\n",
       "      <td>0.492476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.506451</td>\n",
       "      <td>0.493549</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EEM       EWG  TIP  EWJ  EFA  IEF  EWQ  EWU  XLB  XLE  ...  \\\n",
       "Date                                                                    ...   \n",
       "2008-01-02  0.500000  0.500000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2008-01-03  0.500000  0.500000  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2008-01-04  0.502947  0.497053  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2008-01-07  0.500259  0.499741  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2008-01-08  0.501867  0.498133  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "...              ...       ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2016-06-27  0.499579  0.500421  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2016-06-28  0.502483  0.497517  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2016-06-29  0.504671  0.495329  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2016-06-30  0.507524  0.492476  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2016-07-01  0.506451  0.493549  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "            XLU  EPP  FXI  VGK  VPL  SPY  TLT  BND  CSJ  DIA  \n",
       "Date                                                          \n",
       "2008-01-02  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-03  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-04  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-07  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-08  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "2016-06-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-29  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-07-01  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2141 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_and_half_bah.all_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, the stock prices are exactly split in half, but we see that after the first two days the prices diverge in different directions. Although we originally had half of our capital in EEM and EWG, in the end, our portfolio weights have shifted because the underlying prices for EEM and EWG have changed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Best Stock\n",
    "\n",
    "Best Stock strategy chooses the best performing asset in hindsight.\n",
    "\n",
    "The best performing asset is determined with an argmax equation stated below. The portfolio selection strategy searches for the asset that increases the most in price for the given time period.\n",
    "\n",
    "$b_0 = \\underset{b \\in \\Delta_m}{\\arg\\max} \\: b \\cdot \\left(\\overset{n}{\\underset{t=1}{\\bigodot}}  x_t \\right)$\n",
    "\n",
    "Once the initial portfolio has been determined, the final weights can be represented as buying and holding the initial weight.\n",
    "\n",
    "$S_n(BEST) = \\underset{b \\in \\Delta_m}{\\max} b \\cdot \\left(\\overset{n}{\\underset{t=1}{\\bigodot}}  x_t \\right) = S_n(BAH(b_0))$\n",
    "\n",
    "Best Stock strategy can be called using **BestStock()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_stock = BestStock()\n",
    "best_stock.allocate(stock_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
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       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
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       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            EEM  EWG  TIP  EWJ  EFA  IEF  EWQ  EWU  XLB  XLE  ...  XLU  EPP  \\\n",
       "Date                                                          ...             \n",
       "2008-01-02  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-03  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-04  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-07  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-08  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2016-06-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-29  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-07-01  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "            FXI  VGK  VPL  SPY  TLT  BND  CSJ  DIA  \n",
       "Date                                                \n",
       "2008-01-02  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-03  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-04  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-07  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-08  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "2016-06-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-29  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-07-01  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2141 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_stock.all_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EEM    0.0\n",
      "EWG    0.0\n",
      "TIP    0.0\n",
      "EWJ    0.0\n",
      "EFA    0.0\n",
      "IEF    0.0\n",
      "EWQ    0.0\n",
      "EWU    0.0\n",
      "XLB    0.0\n",
      "XLE    0.0\n",
      "XLF    0.0\n",
      "LQD    0.0\n",
      "XLK    1.0\n",
      "XLU    0.0\n",
      "EPP    0.0\n",
      "FXI    0.0\n",
      "VGK    0.0\n",
      "VPL    0.0\n",
      "SPY    0.0\n",
      "TLT    0.0\n",
      "BND    0.0\n",
      "CSJ    0.0\n",
      "DIA    0.0\n",
      "Name: 2016-07-01 00:00:00, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(best_stock.all_weights.iloc[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we examine all of the weights assigned to best_stock, we notice that all the weights are set to 0 except for XLK. For the given period and price data, XLK was the best performing asset, so the portfolio strategy chooses to allocate all of its on weight own XLK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_stock.portfolio_return.plot(title='Best Stock', figsize=(18,8)).set_ylabel('Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen with the above graph, we are directly tracking XLK, the best performing ETF during this period. The same exact graph can be replicated by buying only XLK from the beginning using the Buy and Hold strategy since we are not rebalancing the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bah_best_stock = BAH()\n",
    "# buy_XLK represents a portfolio weight with 1 on XLK and 0 otherwise\n",
    "buy_XLK = np.array(best_stock.all_weights.iloc[0])\n",
    "bah_best_stock.allocate(stock_prices, weights=buy_XLK)\n",
    "bah_best_stock.portfolio_return.plot(title='Using Buy and Hold to represent Best Stock', figsize=(18,8)).set_ylabel('Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Constant Rebalanced Portfolio\n",
    "\n",
    "Constant Rebalanced Portfolio rebalances to a certain portfolio weight every time period. This particular weight can be set by the user, and if there are no inputs, it will automatically allocate equal weights to all assets. The total returns for a CRP can be calculated by taking the cumulative product of the weight and relative returns matrix.\n",
    "\n",
    "$S_n(CRP(b)) = \\overset{n}{\\underset{t=1}{\\prod}} \\:  b^{\\top}x_t$\n",
    "\n",
    "Constant Rebalanced Portfolio can be called with **CRP()**, and the weights can be initiated with CRP(weight=weight). If no weights are specified, it will initialize to uniform weights across all time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
       "      <th>IEF</th>\n",
       "      <th>EWQ</th>\n",
       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EEM       EWG       TIP       EWJ       EFA       IEF  \\\n",
       "Date                                                                     \n",
       "2008-01-02  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-03  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-04  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-07  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-08  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2016-06-27  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-06-28  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-06-29  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-06-30  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-07-01  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "\n",
       "                 EWQ       EWU       XLB       XLE  ...       XLU       EPP  \\\n",
       "Date                                                ...                       \n",
       "2008-01-02  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2008-01-03  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2008-01-04  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2008-01-07  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2008-01-08  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2016-06-27  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2016-06-28  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2016-06-29  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2016-06-30  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "2016-07-01  0.043478  0.043478  0.043478  0.043478  ...  0.043478  0.043478   \n",
       "\n",
       "                 FXI       VGK       VPL       SPY       TLT       BND  \\\n",
       "Date                                                                     \n",
       "2008-01-02  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-03  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-04  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-07  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2008-01-08  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2016-06-27  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-06-28  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-06-29  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-06-30  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "2016-07-01  0.043478  0.043478  0.043478  0.043478  0.043478  0.043478   \n",
       "\n",
       "                 CSJ       DIA  \n",
       "Date                            \n",
       "2008-01-02  0.043478  0.043478  \n",
       "2008-01-03  0.043478  0.043478  \n",
       "2008-01-04  0.043478  0.043478  \n",
       "2008-01-07  0.043478  0.043478  \n",
       "2008-01-08  0.043478  0.043478  \n",
       "...              ...       ...  \n",
       "2016-06-27  0.043478  0.043478  \n",
       "2016-06-28  0.043478  0.043478  \n",
       "2016-06-29  0.043478  0.043478  \n",
       "2016-06-30  0.043478  0.043478  \n",
       "2016-07-01  0.043478  0.043478  \n",
       "\n",
       "[2141 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crp = CRP()\n",
    "crp.allocate(stock_prices)\n",
    "crp.all_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weights stay constant over the given time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crp.portfolio_return.plot(title='Constant Rebalanced Portfolio', figsize=(18,8)).set_ylabel('Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a way, CRP performs a passive mean reversion. For the prices that decrease, the following period's weight should decrease. However, by allocating weights to even out and return to the predetermined number, the portfolio consistently shifts weights from increasing assets to decreasing assets. However, the returns portrayed with CRP is disappointing as well. The change from 1.0 to 1.1 over 8 years is not an optimal portfolio.\n",
    "\n",
    "We will also examine an example of CRP with predetermined weights set by the user. We will use the same weights used in the Buy and Hold strategy that allocates half of the weight to each of the first two assets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,\n",
       "       0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_and_half"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "half_and_half_crp = CRP(half_and_half)\n",
    "half_and_half_crp.allocate(stock_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
       "      <th>IEF</th>\n",
       "      <th>EWQ</th>\n",
       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2008-01-03</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-04</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-07</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-08</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            EEM  EWG  TIP  EWJ  EFA  IEF  EWQ  EWU  XLB  XLE  ...  XLU  EPP  \\\n",
       "Date                                                          ...             \n",
       "2008-01-02  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-03  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-04  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-07  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-01-08  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2016-06-27  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-28  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-29  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-30  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-07-01  0.5  0.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "            FXI  VGK  VPL  SPY  TLT  BND  CSJ  DIA  \n",
       "Date                                                \n",
       "2008-01-02  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-03  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-04  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-07  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2008-01-08  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "2016-06-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-29  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-06-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2016-07-01  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2141 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_and_half_crp.all_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the weights stay constant, which is different from the results from the changing weights of Buy and Hold. The portfolio constantly rebalances itself to the predetermined weight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Best Constant Rebalanced Portfolio\n",
    "\n",
    "Best Constant Rebalanced Portfolio is a strategy that is implemented in hindsight, which is similar to Best Stock. It uses the same weight for all time periods. However, it determines those weights by having the complete market sequence of the past. The objective function for BCRP looks to maximize portfolio returns with the equation below.\n",
    "\n",
    "$b^{\\bf{\\star}} = \\underset{b^n \\in \\Delta_m}{\\arg\\max} \\: S_n(CRP(b)) = \\underset{b \\in \\Delta_m}{\\arg\\max} \\overset{n}{\\underset{t=1}{\\prod}} \\:  b^{\\top}x_t$\n",
    "\n",
    "Once the optimal weight has been determined, the final returns can be calculated by using the CRP returns equation.\n",
    "\n",
    "$S_n(BCRP) = \\underset{b \\in \\Delta_m}{\\max} \\: S_n(CRP(b)) = S_n(CRP(b^{\\bf \\star}))$\n",
    "\n",
    "Best Constant Rebalanced Portfolio strategy can be called with **BCRP()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcrp = BCRP()\n",
    "bcrp.allocate(stock_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EEM    0.000000\n",
      "EWG    0.000000\n",
      "TIP    0.000000\n",
      "EWJ    0.000000\n",
      "EFA    0.000000\n",
      "IEF    0.000000\n",
      "EWQ    0.000000\n",
      "EWU    0.000000\n",
      "XLB    0.000000\n",
      "XLE    0.000000\n",
      "XLF    0.000000\n",
      "LQD    0.000000\n",
      "XLK    0.625272\n",
      "XLU    0.000000\n",
      "EPP    0.000000\n",
      "FXI    0.000000\n",
      "VGK    0.000000\n",
      "VPL    0.000000\n",
      "SPY    0.000000\n",
      "TLT    0.374728\n",
      "BND    0.000000\n",
      "CSJ    0.000000\n",
      "DIA    0.000000\n",
      "Name: 2008-01-02 00:00:00, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
       "      <th>IEF</th>\n",
       "      <th>EWQ</th>\n",
       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.374728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.374728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            EEM  EWG  TIP  EWJ  EFA  IEF  EWQ  EWU  XLB  XLE  ...  XLU  EPP  \\\n",
       "Date                                                          ...             \n",
       "2008-01-02  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-07-01  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "            FXI  VGK  VPL  SPY       TLT  BND  CSJ  DIA  \n",
       "Date                                                     \n",
       "2008-01-02  0.0  0.0  0.0  0.0  0.374728  0.0  0.0  0.0  \n",
       "2016-07-01  0.0  0.0  0.0  0.0  0.374728  0.0  0.0  0.0  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bcrp.all_weights.iloc[0])\n",
    "bcrp.all_weights.iloc[[0,-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from above, the weights are the same from the beginning of the time period to the end. BCRP returns weights of 0.625272 to XLK, which was the best performing ETF, and 0.374728 to TLT, the second-best performing ETF.\n",
    "\n",
    "Interestingly, TLT tracks US Treasury bonds of 20+ years and XLK tracks the S&P 500 technology sector. There might be a passive mean reversion with these two assets that allow the Constantly Rebalanced Portfolio to produce returns that are higher than the Best Stock of XLK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcrp.portfolio_return.plot(title='Best Constant Rebalanced Portfolio', figsize=(18,8)).set_ylabel('Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BCRP achieves higher returns than any other portfolio that we have seen so far. However, this is primarily due to the fact that we had the data of the complete market sequence. This is impossible to implement in the real market, but it is a good benchmark to keep in hand as we implement other strategies. For other portfolios and the given dataset, we should look to perform better than the 1.8 given with BCRP strategy.\n",
    "\n",
    "On another note, our strategies can be resampled to a given time period if we want to simulate a portfolio manager's monthly rebalancing or if we want to minimize our transaction costs. They can be called using **.allocate(resample_by='time')**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EEM    0.00000\n",
      "EWG    0.00000\n",
      "TIP    0.00000\n",
      "EWJ    0.00000\n",
      "EFA    0.00000\n",
      "IEF    0.00000\n",
      "EWQ    0.00000\n",
      "EWU    0.00000\n",
      "XLB    0.00000\n",
      "XLE    0.00000\n",
      "XLF    0.00000\n",
      "LQD    0.00000\n",
      "XLK    0.89725\n",
      "XLU    0.00000\n",
      "EPP    0.00000\n",
      "FXI    0.00000\n",
      "VGK    0.00000\n",
      "VPL    0.00000\n",
      "SPY    0.00000\n",
      "TLT    0.10275\n",
      "BND    0.00000\n",
      "CSJ    0.00000\n",
      "DIA    0.00000\n",
      "Name: 2008-01-31 00:00:00, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "monthly_bcrp = BCRP()\n",
    "# 'M' represents monthly rebalancing\n",
    "monthly_bcrp.allocate(stock_prices, resample_by='M')\n",
    "print(monthly_bcrp.all_weights.iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weights have changed to 0.89725 of XLK and 0.10275 TLT as we are now resampling on a monthly basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEM</th>\n",
       "      <th>EWG</th>\n",
       "      <th>TIP</th>\n",
       "      <th>EWJ</th>\n",
       "      <th>EFA</th>\n",
       "      <th>IEF</th>\n",
       "      <th>EWQ</th>\n",
       "      <th>EWU</th>\n",
       "      <th>XLB</th>\n",
       "      <th>XLE</th>\n",
       "      <th>...</th>\n",
       "      <th>XLU</th>\n",
       "      <th>EPP</th>\n",
       "      <th>FXI</th>\n",
       "      <th>VGK</th>\n",
       "      <th>VPL</th>\n",
       "      <th>SPY</th>\n",
       "      <th>TLT</th>\n",
       "      <th>BND</th>\n",
       "      <th>CSJ</th>\n",
       "      <th>DIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            EEM  EWG  TIP  EWJ  EFA  IEF  EWQ  EWU  XLB  XLE  ...  XLU  EPP  \\\n",
       "Date                                                          ...             \n",
       "2008-01-31  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-02-29  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-03-31  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-04-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2008-05-31  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "...         ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2016-03-31  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-04-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-05-31  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-06-30  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2016-07-31  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "            FXI  VGK  VPL  SPY      TLT  BND  CSJ  DIA  \n",
       "Date                                                    \n",
       "2008-01-31  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2008-02-29  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2008-03-31  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2008-04-30  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2008-05-31  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "...         ...  ...  ...  ...      ...  ...  ...  ...  \n",
       "2016-03-31  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2016-04-30  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2016-05-31  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2016-06-30  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "2016-07-31  0.0  0.0  0.0  0.0  0.10275  0.0  0.0  0.0  \n",
       "\n",
       "[103 rows x 23 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_bcrp.all_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PAAAAAAC8xRizs77tDLsAAAAAAAAeRfIBAAAAAAB4FMkHAAAAAADgUV1uzof6VFVVKSMjQ+Xl5d4OpVMLDAxUTEyM/P39vR0KAAAAAOAw0i2SDxkZGQoJCdHQoUNljPF2OJ2StVZ79+5VRkaGYmNjvR0OAAAAAOAw0i2GXZSXlysyMpLEQyOMMYqMjKR3CAAAAACgw3WL5IMkEg/NwDUCAAAAAHhDt0k+eJuvr68SExOVkJCg008/XQUFBY2WLygo0FNPPdVB0QEAAAAA4D0kH9pJUFCQVq9erZSUFEVEROjJJ59stHxrkw81NTWtDREAAAAAAK8g+eABRxxxhDIzMw/8/eCDD2rKlCkaP3687r33XknSXXfdpW3btikxMVF/+MMftGTJEs2ZM+fAMTfddJNeeuklSdLQoUN15513atKkSXr77bc1a9Ys3XnnnZo6dari4+O1bNkySdL69es1depUJSYmavz48UpNTe24kwYAAAAAoAHdYrULd3//eL02ZBW1a51jokN17+ljm1W2pqZGX331la6++mpJ0qJFi5Samqrly5fLWqszzjhDS5cu1QMPPKCUlBStXr1akrRkyZJG642MjNSqVaskSXPnzlV1dbWWL1+uTz75RH//+9/15Zdfau7cubr11lt16aWXqrKykl4SAAAAAIBOodslH7xl//79SkxMVGZmpkaPHq0TTjhBkjP5sGjRIk2cOFGSVFJSotTUVA0ePLhF9V944YUH/X3OOedIkiZPnqwdO3ZIcva4uO+++5SRkaFzzjlHI0aMaONZAQAAAADQdt0u+dDcHgrtrXbOh7KyMp100kl68skndcstt8haqz/96U+6/vrrDypfmzCo5efnJ4fDceDvuktiBgcHH/R3jx49JDknuqyurpYkXXLJJZo2bZoWLlyoU089Vc8884yOO+649jpFAAAAAABahTkf2lnPnj313//+Vw8//LCqq6t10kkn6cUXX1RJSYkkKTMzUzk5OQoJCVFxcfGB44YMGaINGzaooqJCBQUF+uqrr1rcdlpamoYNG6ZbbrlFZ555ptauXdtu5wUAAAAAQGt1u54PncHEiRM1fvx4vfHGG/rVr36ljRs36ogjjpAk9erVS6+++qqGDx+uGTNmKCEhQaeccooefPBBXXDBBUpISFBsbOyBYRot8dZbb2nevHny9/dX//79dffdd7f3qQEAAAAA0GLGWuvtGFokKSnJJicnH7Rt48aNGj16tJci6lq4VgAAAAAATzHGrLTWJtXdzrALAAAAAAC6iK7WgaAWyQcAAAAAALqA55eladZDS1RV42i6cCdD8gEAAAAAgC5g0fps7dxbpqVbcr0dSot1m+RDV+160pG4RgAAAADQNVVU12h1RoEk6YPVWe1S593vr9Pt81drd+H+dqmvMd0i+RAYGKi9e/dyc90Ia6327t2rwMBAb4cCAAAAAGihtRmFqqx2aFBEkL7YsEclFdVtqi8tt0Sv/7RL7/2cqeMf/kZPL9mmiuqador2UN1iqc2YmBhlZGQoN7frdT3pSIGBgYqJifF2GAAAAACAFlq+PV+S9JfTxui6eSu1aP0enTOp9fd3byVnyNfH6K3rp2vuN2n612eb9HZyuv56+hjNGhnVXmEf0C2SD/7+/oqNjfV2GAAAAAAAeMSKHfka3jdYJ4zpp5jwIH2wOqvVyYfqGofeXZWhY0f21eQhEXru8ggt3pyj//fxBl35vxU6YUw//XXOGA2K6NmieiurG54Is1skHwAAAAAA6K5qHFYrd+zTnAkDZIzRmYnRenrJNuUWV6hvSI8W17d4c65yiyt0QdKgA9uOHRmlI4dH6oVvt+vxr7Zq9iPf6IZjhus3s4Yr0N/3kDoqqmu0eU+x1mUWKiWzUOsyC7V5T3GDbZJ8AAAAAACgE9u0p0jFFdWaMjRCknRW4kA9uXibFqzN0q9ntHwUwFvJ6erTq4eOHXXw8Ioefr767aw4nT1xoO7/ZJMe+ypV767K0J9PG60BYUEHJRq2ZBerqsY572JYkL/GDQzT1UcN058aaJPkAwAAAAAAnVjyjn2SdCD5MKJfiMYMCNUHq1uefMgpLtfXm3J0zcxY+fvWvwbFgLAgPX7xRF0ydbD+9tF63fDqqgP7evd0JhqumTlM4waGadzAMMWEB8kYI0kkHwAAAAAAaEyNw2pdZqFi+wQrLMjf2+EcsHxHvgaEBSomPOjAtrMmRuv+TzZpe16pYvsEN7uu91ZlqsZhDxpy0ZAjhkdq4S1HaeG63Qrw9VFCnURDS5B8AAAAAAActsqravTd1jwtWp+tLzdma29ppZKGhGv+9UfI16flN9ntzVqrFdvzNX1Y5EE3/WdMGKj/+3STPlydqdtmxze7rrdWpGvK0HAN79urWcf4+frozMSBrYr9oHraXAMAAAAAAF1I4f4qLd6Uo0Ub9mjJ5lyVVdaoVw8/HTsqStFhgXpmaZrmfrNNNx4b5+1QtSu/TDnFFZoSG3HQ9v5hgZoeG6kPV2fp1uNHNKs3QvLOfUrLK9UNs4Z7KtwGkXwAAAAAAHR7BWWV+njtbi1av0c/bNuraodV35AeOmviQJ04pp+OGB6pHn6+stYqY99+/eeLLTomvq8SBoZ5Ne7l2/MlSVOHRhyy76yJ0brz3XVam1GoCYN6N1nX/BXpCg7w1WnjBrR7nE0h+QAAAAAA6PZufuNnLUvNU2yfYF09M1Ynje2vxJje8qkztMIYo/vOTlDyznzd+ubPWnDzTAUFHLrUZEdZsSNfYUH+GhF16DCJkxMG6C8frNcHqzObTD6UVFRr4drdOjMxWsE9Oj4VUP/UlgAAAAAAdBPVNQ4t356vK44Yoq9/f4z+dMpoTRocfkjioVbvngF66PwJ2pZbqgc+3djB0R5sxY59mjK0/ljDgvx13Kgofbxmt6prHI3Ws2BNlvZX1eiCKU1PNOkJJB8AAAAAAN3attxSVVQ7NHFweLNXapg5oq9+PWOoXv5hp77ZkuvhCOuXU1yu7XmlB5bYrM9ZE6OVV1Kh77ftbbSu+cnpGhHVSxObMTzDE0g+AAAAAAAaZK3Vj2l7VVxe5e1QWi0ls1CSlDAwtEXH3XnyKI2I6qU/vL1G+0orPRFao5J37JOkQyabdDdrZJRCAv30werMBsukZhfr510FunDKoFYtk9keSD4AAAAAQBfx4rfbddJ/lqq8qqZD2qusduieD1J00bM/6v8+3dQhbXpCSlahgvx9FdunectL1gr099WjFyVqX1ml7n5/nay1Hoqwfsu35yvQ30cJ0Q1Pehno76tTEwbo85Q92l9Z//ti/op0+fkYnTWx7UtmthbJBwAAAADoIj5YnanN2cV6Y/kuj7e1t6RClz3/k17/aZcG9g7Sx2uyOizp0d7WZxZpTHSofBuY46ExY6PDdPsJI/Vpyh69u6rh3gWekLwzX4mDeivAr/Fb9zMnRqu0skZfbsw+ZF9ltUPv/Zyp2aP7qU+vHp4KtUkkHwAAAACgC8gvrdS6zEL5GOmpJds8mgjYkFWkM574TmsyCvTYRYn693njVVxerS82HHpz29k5HFbrswqVEN2yIRfurjt6mKYOjdDfPlqv9PyydoyuYcXlVdqQVVTvEpt1TY+NVP/QQH1Yz9CLrzdlK7+0Uhd6aaLJWiQfAAAAAKAL+HZrnqyV/nDSKOUWV+i1nzzT++HTdbt17tPfq8Zh9fYNR+jMxIE6YlikosMC9c7KDI+06Uk79paqtLJGYwc2PHShKb4+Rg9fMEGSdPtbq1Xj8Pzwi1W7CuSwjc/3UMvHx+iMxGgt2Zx7yNwU81ekq39ooI6O7+upUJuF5AMAAAAAdAHLtuQqLMhf1x09TNOHRejpJdsaHOPfGg6H1X++2KLfvLZKowaE6KObZmh8jHNlBB8fo3MmxWhZaq6yi8rbrc2OkJJVJEka14bkgyQNiuipv58xVit27NMzS7e1R2iNWrE9X74+RpMGhzer/JmJ0ap2WC1ct/vAtj2F5fpmS67OmxzTqiEn7YnkAwAAAAB0ctZaLU3N1VFxfeTrY/S72fHKK6nQaz/tbJf6Syuq9dvXVumxr1J17qQYvXHtdEWFBh5U5tzJMXJY6f2fO3beg7Zan1moAD8fxUW1bLLJ+pwzaaBOHddfjyzacmAFDU9ZviNfY6NDFdzDr1nlxwwI1YioXgcNvXhnZbocVjo/KcZTYTYbyQcAAAAA6ORSc0qUXVShmSP6SJKmDYvUjLhIzf1mm8oqq9tUd3p+mc59+nst2rBHfz5ttB46f7wC/X0PKRfbJ1hJQ8L1zsqMDl/1oS1Ssgo1un+I/H3bfvtrjNF9Z41TZK8A3TZ/tcfm3aiortHq9AJNacZ8D+6xnTVxoFbs2KeMfWVyOKzeSs7Q9GERGhIZ7JE4W4LkAwAAAAB0cku35EqSZrqN23f2fqjUqz+2vvfD8u35OvPJ75RZsF//+/VUXTNzmIxpuHv+uZNjtDWnRGsyPPurf3ux1iols6hN8z3UFR4coIfOn6CtOSX6zxdb2q1ed+syClVZ7WhR8kGSzpgQLUn6cHWWfty+V7vyy7w+0WQtkg8AAAAA0MktTc3T8L7BGtg76MC2pKERmjmij575Jq1VvR9SMgt1xYvL1bunvz68cYaOacaEhKeNH6Aefj56t4tMPJmxb78K91cpIbr9kg+SNHNEX12QFKPnv92uTXuK2rVuyTnkQpKmDG3efA+1BkX0VNKQcH24OlNvrUhXSKCfTkkY0O7xtQbJBwAAAADoxMqravRT2t56Vyu4bXa89pZW6pUfWtb7Iae4XNe+kqzePf315nXTNaxv8+ZDCA3018kJ/fXRmqxWDznYkVeqW9/8WUXlVa06viXWZzl7aCQMbP0ymw350ymjFRrop3veT5GjnVe/WLE9X8P7BiuyV48WH3vmxIHakl2ij9fu1pmJ0fUOofEGjyUfjDEvGmNyjDEpDewPM8Z8bIxZY4xZb4z5tadiAQAAAICuasWOfFVUO3T0iEOTD5OHhOvo+L56dmmaSiua1/uhvKpG172yUgVlVXru8iRFhQQ2fZCbcyfFqHB/lb7amNOi4yTnMIh7PlinD1dnaeHa3U0f0EYpmUXy8zGK7xfS7nWHBwfo7lNHa+XOfZqfnN5u9TocVsk792lqM5bYrM9p4wbIz8eoxmF1YdLgdourrTzZ8+ElSSc3sv9GSRustRMkzZL0sDEmwIPxAAAAAEC7WZdR6LEJB90tS81TgK+Ppg2r/2b0d7NHKL+0Ui//sKPJuqy1uuvdtVqdXqD/XDhBCa2YC2FGXB/1Dw3Uu6taPvTi8/V79N3WvfLzMVqwNqvFx7dUSlahRvQL8div/+dNjtHU2Ag98Okm5ZVUtEudm7OLVVxe3eL5HmpFBAfo5IT+ShzU2yM9PlrLY8kHa+1SSfmNFZEUYpyzmfRylW3bNK0AAAAA0AFSMgt1+hPf6jevrlRNO3e5r2vpllwlDQ1Xz4D6l1ycODhcs0Y6ez8UNzGU4akl2/TB6iz9/oR4ndzKuQB8fYzOmTRQ32zJVU5RebOP219Zo38s2KhR/UN07dHD9MO2vcotbp8b9vo4J5ssVEK0527AjTG6/+wElVVW6/5PNrZLnSsOzPfQuuSDJP3nwkTNv356o5OHdjRvzvnwhKTRkrIkrZN0q7XW4cV4AAAAAKBZ5q9IlzHS4s25enjRZo+1k1NUrk17iuud78Hd72bHq6CsSi9/v6PBMp+v36MHP9+sMyZE66bj4toU17mTY1TjsPpgdWazj5n7zTZlFuzX384YqzMTo+Ww0mcpnht6kV1UobySylb17miJuKgQXXf0ML23KlPfb8trc33Lt+erf2igYsKDmi7cAH9fH/Xw6xxzPdTyZvLhJEmrJUVLSpT0hDGm3pSUMeY6Y0yyMSY5Nze3I2MEAAAAgIPsr6zRB6szdeaEaF08dbCeWrJNH6/xzBCCpanOm9mZI/o0Wm7CoN46flSUnlu2vd6JHDdkFel381drQkyY/n3e+Db/Ij68by9NHNxb767MlLVN9/xIzy/T3G+2ac74AZo+LFIj+4UoLqqXPvbgvA8pmZ6bbLKum44doUERQfrzBymqqG79UBxrrVbsyE6umBwAACAASURBVNeU2IhO1WuhPXgz+fBrSe9Zp62StksaVV9Ba+2z1toka21S375NL/8CAAAAAJ7yybrdKi6v1kVTB+vvZ4xV0pBw/eGdNQdudtvTstRc9enVQ6P7N30DfdvseBXur9JL3+04aHtucYWueXmFQgP99dzlSe02/8G5k2K0ObtYKZlNLzX5z4Ub5GOM7jlttCTncIU54wdoxY58Zbdg6EZLpGQVyhhp9ADPJx+CAnz1jzMTlJZbqme/SWt1Pen5+5VdVKGpLVxisyvwZvJhl6TjJckY00/SSEmtf5UAAAAAoAPMX5Gu2D7BmhYboQA/Hz192WSF9wzQ9fNWttukg5Jz1YNlqXmaOaKPfHya/hV8XEyYZo/up+eXpalwv7P3Q3lVja6fl6z8sko9f0WSokJbtrJFY04fH60APx+9s7LxlR6Wpebq8/XZuum4OA0I+2UowZzx0bJWHlv1IiWzSMP79mpwroz2NmtklE4bN0CPL96qHXmlrapjee18D61c6aIz8+RSm29I+kHSSGNMhjHmamPMDcaYG1xF/iHpSGPMOklfSbrTWtv2ATIAAAAA4CHbcku0fEe+Lpwy6EC3+L4hPfTMryYrr6RCv31tlapq2mcquw27i5RfWqmj4xsfcuHuttkjVFRerf99t13WWt393jqt2lWgRy5IbPe5D8J6+uvEMf304ZqsBocaVNU49PePN2hIZE9dMzP2oH1xUb00qn+Ix1a9WJ/l2ckm6/PX08cowNdHf/kwpVnDUepasT1fYUH+io9q/6VBvc2Tq11cbK0dYK31t9bGWGtfsNbOtdbOde3PstaeaK0dZ61NsNa+6qlYAAAAAKA9zF+RLj/Xag/uxsf01r/OHa/l2/P1/z7e0C5tfbPFOd/dUXHNH3qeMDBMJ47ppxe+3a6HFm3Wez9n6nez43XquNatbNGUcyfHqKCsSos35dS7/+Xvd2hrTon+OmdMvRMgnj4hWqt2FSizYH+7xpVXUqHdheUen2yyrn6hgbrjxHgtS83Tglb06FixI19JQ8Kb1dOlq/HmsAsAAAAA6DIqqx16d2WGjh8dpaiQQ4cvnDVxoK4/epjm/bhTr/+0q83tLUvN1ZgBoeob0qNFx902O17F5dV6crFzgsdbjm/byhaNmRnXR1EhPfTOyoxD9uUUl+vRL1N17Mi+On50v3qPnzPemRRZ2M69H9ZnOeehGBvdsckHSfrVEUM1bmCY/t+CDQeGvzRHbnGF0vJKu+WQC4nkAwAAAAA0y1cbs7W3tFIXTR3cYJk/njxKx8T31b0fpSjZNX6/NUorqrVy5z7NbMGQi1pjokN18dTBOnJ4pB46f4JHV03w8/XR2ZMGavHmXOUWHzzfxb8+3ayK6hr9Zc6YBo8fEhmscQPDWtVLoDG1k3+O6eBhF5Lk62N039kJ2ltS0aJlWFfudM33MJTkAwAAAAActt5cka4BYYE6ekTDwyB8fYz+e9FExYT31A2vrlJWK4cT/Ji2V1U1Vsc00lZj7j87Qa9fO73dVrZozHmTYlTjsPpwdeaBbSt37tO7qzJ09VHDNKxvr0aPnzN+gNZmFGrn3tZN0lif9VmFGhLZU2FB/u1WZ0uMj+mty48Yqnk/7tSa9IJmHbN8+z4F+vtoXAcPFekoJB8AAAAAoAkZ+8q0NDVX5ycNkm8T4/HDevrrucsnu1aaWKnyqvonY2zM0i25CvL31eRWLrnoyd4OdY3oF6IJMWEHhl44HFZ/+2i9+oX20M3HNT3k47TaoRfr2q/3Q0pmkRK8MOTC3e0nxqtvrx66+/112ppT3OREpCt25CtxUG8F+HXP2/TueVYAAAAA0I7eTnbeWF+QFNOs8nFRIXr0wkSlZBXqrnfXtnjlg2WpeZo+LKLeSRo7o/Mmx2jTnmKtzyrUW8npWpdZqLtPHa3gHk0vcxkT3lMTB/fWgjXtk3woLKvSrvwyjR3Y8UMu3IUG+utvZ4zV+qwizX5kqcb89TOd+J9vdOPrq/Tol1v0ybrdSs12JiVKKqq1PqtQU7vpkAtJ6pgFTwEAAACgi6pxWL2dnK6ZI/oqJrxns4+bPaaffn9CvB5atEWjBoTqhmOGN+u49PwypeWV6rLpQ1obcoc7fUK0/rFgo174druWbM7V1KEROmNCdLOPnzM+Wv9YsEFpuSVNDtNoyvrdzvkevN3zQZJOHTdAi353tNZlFCo1p0Rbc4q1LqNQn6zbrdp8lJ+PUb/QQDmsuu1kkxLJBwAAAABo1LLUXGUVluvPjUyc2JAbj43Txj3F+tdnmxTfr5eOG1X/qg8Ht5cnSTo6vnXzPXhD754Bmj0mSu+typSPkf52xtgWDf04bdwA/XPhBi1Yu1u3HD+iTbGsz6xd6cK7PR9qxfcLUXy/kIO27a+s0bbcEqXmFCs1u0SpOSUaHtVLSUNIPgAAAADAYenN5emKCA7Q7AaWi2yMMUYPnTdBO/JKdcsbq/XBjUcqLiqk0WOWbslVdFighvcNbm3IXnHe5Bh9sm6PLp02pMWrTPQPC9SUIRFasDarzcmHlKxCRYcFKrJXy5Yo7UhBAb5KGBimhG46uWR9mPMBAAAAABqQW1yhLzdm69xJA1s9EWBQgK+euzxJgf6+uvrlZBWUVTZYtrrGoe+25eno+L4dOmlke5gVH6VHL0zUXaeMatXxcyYM0JbsEm3JLm5THCmZhRp7GN3UdxUkHwAAAACgAe+tylC1w+rCKYPbVE907yA986vJ2l1QrhtfX9XgygdrMgpUXF6tma1cYtObfHyMzpo4sFmTTNbn5IT+8jHSgjVZrY6htKJaaXmlnWK+BxyM5AMAAAAA1MNaq/kr0jVlaLjioto2CaIkTR4SrvvOTtB3W/fqvoUb6y2zdEuefIw0Iy6yze11NVEhgZoWG6kFa3e3eHWQWht3F8laKcHLK13gUCQfAAAAAKAey7fnKy2vtM29HtydnzRI1xwVq5e+36E3lu86ZP+y1FyNj+mt3j0D2q3NrmTOhAFKyyvVht1FrTp+XaZzpYtxDLvodEg+AAAAAEA95q9IV0gPP506rn+71nvXKaN0dHxf/eWDFP2UtvfA9sKyKq1OL+hSq1y0t1MSBsjXx2jB2t2tOj4ls0h9Q3ooKjSwnSNDW5F8AAAAAIA6CvdXaeG63TpzYrR6BrTvIoF+vj56/OKJGhzRU795bZXS88skSd9vy5PDSkeP6NOu7XUlEcEBOnJ4pBaszWrV0Iv1WYVK6CRLbOJgJB8AAAAAoI4PV2eqotqhi9pxyIW7sCB/PXdFkqpqHLr2lWSVVlRraWquQnr4KXFQb4+02VWcPj5a6fn7tTajsEXHlVfVKDWn5LBavrIrIfkAAAAAAG6stXpjebrGRod69EZ2eN9eeuKSSdqSXazb31qtpVvydGRcpPx8D+/btJPG9pe/r9GCtS1b9WLTnmLVOKzGstJFp3R4v6sBAAAAoI6UzCJt3F2ki6YM8nhbx8T31d2njtbn67OVWbD/sJ7voVZYT3/NHNFXC9fulsPR/KEXKa7JJlnponMi+QAAAAAAbt5csUuB/j46I3Fgh7R39VGxOn9yjHyMdPQIkg+SNGf8AGUVluvn9H3NPmZ9VqF69/TXwN5BHowMrUXyAQAAAABcvt6UrbdXZui0cdEKC/LvkDaNMXrg3PH66vezNCiiZ4e02dmdMKafAvx89PGa5q96kZJZpIToMBljPBgZWovkAwAAAADIOcnkda+s1Mh+IbrntNEd2ravj1Fsn+AObbMzCwn016z4vlqwdrdyisubLF9Z7dDmPcUay5CLTovkAwAAAIDD3rwfdui2+as1eUi4Xr92miKCA7wd0mHv2qOHqbSiWqc//q1+3tX48IvUnGJV1jiUwGSTnRbJBwAAAABd2oOfb9Jp/12mb1PzWnystVaPf5Wqv3y4XseP6qeXr5qqkMCOGW6Bxk0ZGqH3fnukAvx8dOEzP+qtFekNll2fWSRJLLPZiZF8AAAAANBlbc0p0dxv0pSaU6LLXvhJN762SrsL9zfrWIfD6p8LN+rhL7bonIkDNfeySQr09/VwxGiJ0QNC9dGNR2lqbIT++O5a/fXDFFXVOA4pl5JVqF49/DSEOTM6LZIPAAAAALqsf322SUH+vlpyxyzdfkK8vtyYreMf/kZPL9mmyupDb1JrVdc49Md31+qFb7fryiOH6qHzJ8jPl9ujzig8OEAv/XqKrjt6mF75Yacufe4n5ZVUHFQmJbNQY6JD5ePDZJOdFZ8uAAAAAF3S8u35+mJDtn4za7iiewfpluNH6Mvbj9GMuD7612ebdPJjS+sdilFeVaPfvrZK76zM0G2zR+je08dw09rJ+fn66O5TR+uxixK1NrNApz/+rdZmFEiSahxWG3YXMd9DJ0fyAQAAAECXY63VfZ9sVP/QQF01I/bA9kERPfXc5Ul68cokVdfYQ4ZilFRU66qXVmjRhmz9dc4Y3TY7nqUZu5AzEwfqnRuOlI8xOm/uD3p3ZYbScktUXuVQAitddGp+3g4AAAAAAFpq4brdWpNeoH+fN15BAYfO03DcqH46cngfPbs0TU8u3qrFm3N047FxWrQhWymZhXr4/Ak6d3KMFyJHWyUMDNPHNx+lG19bpd+/vUYTYsIObEfnRc8HAAAAAF1KRXWN/v3ZZo3qH6JzJzWcQAj09z1oKMaDn2/Wxt1FmnvZZBIPXVxEcIDmXT1VV82I1ZqMQgX6+2hYn2Bvh4VG0PMBAAAAQJfy6o+7tCu/TC9fNVW+zZiroXYoxvdb8xQS6K9xMfxC3h34+fror6eP0ZSh4Sour2bC0E6O5AMAAACALqNwf5Ue/zpVM0f00THxfVt07JFxfTwUFbzplHEDvB0CmoHUEAAAAIAu46nFW1W4v0p/OmW0t0MB0AIkHwAAAAB0CRn7yvS/73fonIkxGhPNygZAV0LyAQAAAECX8PCiLTKS7jgp3tuhAGghkg8AAAAAOr2UzEK9/3Omrj4qVgPCgrwdDoAWIvkAAAAAoFOz1ur+TzYqIjhAN8wa7u1wALQCyQcAAAAAndqSzbn6ftte3Xr8CIUG+ns7HACtQPIBAAAAQKdVXePQ/326UUMje+riqYO9HQ6AViL5AAAAAKDTemdlhrZkl+jOk0cpwI/bF6Cr4tMLAAAAoFMqq6zWI19s0eQh4To5ob+3wwHQBiQfAAAAAHQ6+ytr9Pu31iinuEJ3nzpKxhhvhwSgDfy8HQAAAAAAuMsq2K/r5iVrfVaR7jl1tCYPifB2SADaiOQDAAAAgE5j5c58XT9vlSqqavTiFVN07Kgob4cEoB2QfAAAAADQKbydnK573k9RdO9AvXndNMVFhXg7JADthOQDAAAAAK9yLqe5SS98u11HxfXRE5dMVO+eAd4OC0A7IvkAAAAAwGsK91fp5jd+1tItubryyKH682mj5efLvPhAd0PyAQAAAIBXbMst0bUvJyt9X5keOGecLpo62NshAfAQkg8AAAAAOtySzTm6+Y2fFeDro9euma6psaxoAXRnJB8AAAAAdJiyymo99mWqnluWppH9Q/Xc5ZMVE97T22EB8DCSDwAAAAA6xOJNOfrzBynKLNivi6YM0l/mjFFwD25JgMMBn3QAAAAAHpVTVK6/L9ighWt3Ky6ql966/giGWQCHGZIPAAAAADzC4bB6ffku/euzTaqoduj3J8Tr+mOGK8CP1SyAww3JBwAAAADtbtOeIt393jqt2lWgI4dH6r6zxym2T7C3wwLgJSQfAAAAALSb/ZU1+u/XqXpuaZpCg/z1yAUTdPbEgTLGeDs0AF5E8gEAAABAu8gtrtD5c7/Xjr1luiApRn86ZbTCgwO8HRaATsBjyQdjzIuS5kjKsdYmNFBmlqRHJflLyrPWHuOpeAAAAAB41qNfblHGvv167ZppmhHXx9vhAOhEPDnTy0uSTm5opzGmt6SnJJ1hrR0r6XwPxgIAAADAg7bllujNFem6dNpgEg8ADuGx5IO1dqmk/EaKXCLpPWvtLlf5HE/FAgAAAMCz/v3ZJgX5++rm40d4OxQAnZA317iJlxRujFlijFlpjLnci7EAAAAAaKXkHfn6fH22rj96mPr06uHtcAB0Qt6ccNJP0mRJx0sKkvSDMeZHa+2WugWNMddJuk6SBg8e3KFBAgAAAGiYtVb3f7JRUSE9dPXMWG+HA6CT8mbPhwxJn1trS621eZKWSppQX0Fr7bPW2iRrbVLfvn07NEgAAAAADft8/R6t2lWg20+IV88AFtMDUD9vJh8+lHSUMcbPGNNT0jRJG70YDwAAAIAWqKpx6F+fbdaIqF46b3KMt8MB0Il5cqnNNyTNktTHGJMh6V45l9SUtXautXajMeYzSWslOSQ9b61N8VQ8AAAAANrXm8t3aXteqV64Ikl+vt78XRNAZ+ex5IO19uJmlHlQ0oOeigEAAACAZ5RUVOvRL1M1LTZCx42K8nY4ADo5BmUBAAAAaLFnl6Zpb2mlXjh1tIwx3g4HQCdH3ygAAAAALZJTVK7nlqZpzvgBShzU29vhAOgCSD4AAAAAaJH/fJmqaodDfzhppLdDAdBFkHwAAAAA0Gxbc4o1f8UuXTptiIZEBns7HABdBMkHAAAAAM32wKebFRzgp5uPi/N2KAC6EJIPAAAAAJrlp7S9+nJjtm6YNVyRvXp4OxwAXQjJBwAAAABNstbq/k83qX9ooK6aEevtcAB0MSQfAAAAADTpk3V7tCa9QLefGK+gAF9vhwOgi/HzdgAAAAAAOqfdhfu1eleBfk4v0Ps/Z2pkvxCdOynG22EB6IJIPgAAAADQ/soarcss1M+79unnXQVanV6gPUXlkqQAPx8lRIfq3tPHytfHeDlSAF0RyQcAAACgG0rPL9PizTmqrHaossahqmqrypoaVdVYt20OVVQ7tC23RJv2FKvGYSVJQyJ7atqwCE0c1FsTB4dr9IBQBfgxYhtA65F8AAAAALqZiuoaXfTsj8os2H/Qdl8fowBfH/n7GgX4+SrA18jfz0cx4UH67azhShzUW4mDerOSBYB2R/IBAAAA6GbeXJ6uzIL9mnvZZB0ZF+lKOPgwZAKA15B8AAAAALqRsspqPf71Vk2NjdBJY/vJGBIOALyPgVsAAABAN/LS9zuUV1KhP540ksQDgE6D5AMAAADQTRSWVWnukm06blSUkoZGeDscADiA5AMAAADQTTy7bJuKyqt1x4kjvR0KAByE5AMAAADQDeQWV+jFb3fo9AnRGhMd6u1wAOAgJB8AAACAbuDJxVtVWePQ7SfEezsUADgEyQcAAACgi8vYV6bXftqpC5JiFNsn2NvhAMAhSD4AAAAAXdyjX6bKGKNbjh/h7VAAoF4kHwAAAIBOYGtOsR78fJOKyqtafNx7qzJ0+fQhGhAW5KHoAKBt/LwdAAAAAHC4yyku1+UvLFdWYbk+XbdHz16epLioXs069pEvtijI31e/mTXcw1ECQOvR8wEAAADwov2VNbr25WTtK6vSP89KUOH+Kp395Hf6elN2k8euyyjUJ+v26OqZwxTZq0cHRAsArUPyAQAAAPASh8Pq9rdWa21moR67KFGXTR+ij24+SkP69NTVLyfrycVbZa1t8PgHF21W757+unZmbAdGDQAtR/IBAAAA8JJ/f75Zn6bs0T2njtaJY/tLkgb2DtLb1x+p08dH68HPN+vmN35WWWX1Icf+mLZXS7fk6rezhisk0L+jQweAFiH5AAAAAHjB/BW7NPebbbp02mBdfdTBPReCAnz12EWJuuuUUVq4brfOe/oHZewrO7DfWqsHP9+sfqE9dPkRQzs4cgBoOZIPAAAAQAf7bmue7nk/RTNH9NHfzhgrY8whZYwxuuGY4XrxyilK31emM574Tj+m7ZUkLd6co5U79+mW40co0N+3o8MHgBYj+QAAAAB0oK05xbrh1ZUa1jdYT146Sf6+jf+T/NiRUfrwxhkK7+mvy57/SS9/v0MPfr5FQyJ76oKkQR0UNQC0DckHAAAAoIPsLanQVS8lq4efj164YopCmzlXw7C+vfT+jTN0dHxf3fvRem3cXaTbT4hvMnEBAJ2Fn7cDAAAAAA4H5VU1un7eSmUXleuN66ZrUETPFh0fGuiv5y5P0uNfp2pbbqlOHx/toUgBoP2RfAAAAAA8zFqrO99dq+Sd+/TkJZM0aXB4q+rx9TG6bXZ8O0cHAJ5HPy0AAADAwx79MlUfrs7SH04aqdPGD/B2OADQ4Ug+AAAAAB703qoMPfZVqs6bHKPfzhru7XAAwCtIPgAAAAAe8v3WPN357lodMSxS9589rt4lNQHgcEDyAQAAAPCALdnFuv7VlYrtE6y5v5qsAD/+6Q3g8MU3IAAAANDOcorK9ev/rVCgv69evHKKwoKat6QmAHRXJB8AAACAdlRaUa2rXl6hfWWV+t+VUxQT3rIlNQGgOyL5AAAAALST6hqHbnp9lTZkFenJSyYpYWCYt0MCgE7Bz9sBAAAAAN2BtVb3frReizfn6p9nJejYUVHeDgkAOg16PgAAAADt4JmlaXrtp1264Zjhumz6EG+HAwCdCskHAAAAoI0+XpOlBz7dpNMnROuPJ430djgA0OmQfAAAAADaYMWOfP3+rTWaMjRcD543Xj4+xtshAUCnQ/IBAAAAaKVtuSW69pVkxYQH6dlfJSnQ39fbIQFAp0TyAQAAAGiF0opqXfXSCvkao5d+PVXhwQHeDgkAOi1WuwAAAABa4fWfdmnn3jK9ce10DY7s6e1wAKBTo+cDAAAA0ELlVTV6ZmmaZsRF6ojhkd4OBwA6PZIPAAAAQAvNX5GuvJIK3XzcCG+HAgBdAskHAAAAoAUqqms095ttmjI0XNNiI7wdDgB0CSQfAAAAgBZ4b1WmdheW6+bjRsgYltUEgOYg+QAAAAA0U1WNQ08t2aoJMWGaOaKPt8MBgC6D5AMAAADQTB+tzlJ6/n56PQBAC5F8AAAAAJqhxmH15JKtGj0gVMePjvJ2OADQpZB8AAAAAJrhk3W7lZZbqpuPi6PXAwC0EMkHAAAAoAkOh9UTX29VXFQvnTy2v7fDAYAux2PJB2PMi8aYHGNMShPlphhjqo0x53kqFgAAAKAtvtiYrc3Zxbrp2Dj5+NDrAQBaypM9H16SdHJjBYwxvpL+JWmRB+MAAAAAWs1aZ6+HIZE9NWf8AG+HAwBdkseSD9bapZLymyh2s6R3JeV4Kg4AAACgLZZsydW6zELdOCtOfr6MWgaA1vDat6cxZqCksyU97a0YAAAAgMZYa/X4V6ka2DtIZ00c6O1wAKDL8mbq9lFJd1prHU0VNMZcZ4xJNsYk5+bmdkBoAAAAgPTDtr1atatAN8wargA/ej0AQGv5ebHtJElvupYp6iPpVGNMtbX2g7oFrbXPSnpWkpKSkmyHRgkAAIDD1uNfb1VUSA+dPznG26EAQJfmteSDtTa29rkx5iVJC+pLPAAAAADekLwjXz+k7dVf5oxRoL+vt8MBgC7NY8kHY8wbkmZJ6mOMyZB0ryR/SbLWzvVUuwAAAEB7ePzrrYoMDtAlUwd7OxQA6PI8lnyw1l7cgrJXeioOAAAAoKXWpBfomy25uvPkUQoKoNcDALQVs+YAAAAAbhwOq0e/3KKwIH/96ogh3g4HALoFkg8AAADwCoej880jXlxepevmrdTizbm68djh6tXDm/OzA0D3QfIBAAAAHaq6xqH7Fm5Q4v9bpHUZhd4O54Cde0t1zlPfa/HmHP39jLG6duYwb4cEAN0GyQcAAAB0mJzicl3y/E96btl2VdVY3fH2GlVWO9pcb2FZlXbuLW318ctSc3XGE98pt6RC866aqiuOHCrXkvAAgHZA8gEAAAAdYsWOfM3577dam1GgRy9M1JOXTtTm7GI9/nVqm+otr6rRhc/+oGMeXKLrXknW2oyCZh9rrdXzy9J0xYvL1T80UB/deJSOjOvTpngAAIdiEBsAAAA8ylqr/323Q/d/slEx4UF65eqpGtU/VJJ0zqSBemrJNp00tr8SBoa1qv57P1yvzdnFunjqIC1cu1uLNmTrmPi+uvm4OCUNjWjwuPKqGt3zforeXZWhk8b20yMXJCqYOR4AwCOMtZ1vop/GJCUl2eTkZG+HAQAAgGYorajWXe+t08drsnTCmH56+IIJCg30P7C/sKxKJ/znG0UEB+ijm45SgF/LOua+szJDd7y9RjcdG6c7Thqp4vIqzftxp55ftl35pZWaPixCNx07QjPiIg8aRpFTVK7r5q3U6vQC3Xr8CN16/Aj5+DDMAgDayhiz0lqbdMh2kg8AAADwhG25Jbph3kptyy3R708cqd8cM7zeG/yvNmbr6peTdctxcbr9xJHNrn9LdrHOeOJbJQ7qrVevniY/318SF2WV1XpjebqeXbpN2UUVShzUWzcfF6fjRkVpTUahrp+XrOLyaj1ywQSdnDCgXc4XAEDyAQAAAB3os5TduuPttQrw89HjF0/UjCbmUbj9rdX6cHWWPrxxRrOGX5RWVOvMJ79TQVmVPrnlKEWFBtZbrqK6Ru+szNDTS7YpY99+jewXou17S9UvtIeeuzzpwPAPAED7aCj5wISTAAAALZRTVK7XftqpK15crtF/+UyL1u/xdkidRo3D6v8+3agbXl2luKheWnDzUU0mHiTp3jljFRkc0KzVL6y1uuf9dUrLLdF/L05sMPEgST38fHXptCFafMcsPXT+BDms1bTYCH144/9n776jo6zWLoDvM5MO6b0XUiCQhBISQu9IUxFBUFFRFCtW7O1e/exeCyp4RRCR3lRAQemdkFASQkkhlSSk9zLtfH8AXpSWMpNJ2b+1skxm3vecHZaEzDPnPGcgCw9ERC2IHXWIiIiIbkJKibTCKmxNuoA/T13A8eyLpyn4OlrBykyJ7/elY3R3NyOnNL7qeg3mrDiG7WcKcG8/H7wxIRTmJsoG3WtrZYr3JoVhC4lqGAAAIABJREFU1o9x+GpnKp4bFXzda1ceycbPx3Px3Khg9O/SsJMpTJUK3NnHC3f28WrQ9UREpF8sPhARERFdg1YncTy7FH9cKjicK6oGAER42WLumBCMCnVFkEtnLNh9Dh9uOYO0wip0ce5s5NTGU1BRhweXHMGp3Aq8c1t3zIjxa/QYI0NdcUcvT3yzMxWjQ12vuf0iKbccb/2ahEFBTnhyWKAekhMRUUtgzwciIiKif6iq1+COb/Yj+UIVTJUC/QIcMbq7G0Z1c4Wb7d+X+BdW1iPm/e2YOcAPr40PNVJi4zqTX4EHFx9BWa0aX93dC8O7ujZ5rLIaFUZ9tgeO1zj9orJOjYnz9qFOrcPmOQPh2NlcH/GJiEiP2POBiIiIqIHe/+00Ugqq8OHkMMS/MQpLH4rGjH6+VxUeAMDZ2hyjQl2xNj4H9RqtEdIa196UQkyZfxAancTq2THNKjwAgJ2VGd6fFIYz+ZX4emfqX49LKfHyukRkl9Zi3t29WHggImpjWHwgIiIiusKB1CIsO5yFWQP9cVdfH9hYmN70nulRPiitUeOPpAstkLD1WHUkCzMXH4GnvSV+buApFQ0xMtQVk3p54uudqUjKLQcA/HgwE5sT8/DimBD09XPQyzxERNRyWHwgIiIiuqS6XoMX1yUgwKkTnh8d0uD7BgY6wcveEitiswyYrvXQ6SQ+3noGL61LRP9AJ6x5NAYedpZ6neOtiaGw72SGF9YkIC6jBO9uPoURXV3w8KAAvc5DREQtg8UHIiIioks+3HIG58tq8dGd4bAwbdgpDQCgUAhM6+uNA2nFSL/UmLK9qtdo8cyq4/h6Zxqm9fXG9/dHwroBq0May87KDO9NCsPpvApM/+4QXKwt8OnUCCgUQu9zERGR4bH4QERERATg0Lli/HgwEzP7+yOyCcv6p0R6Q6kQWHmk/a5+KK1WYcbCWPx6Ihcv3hKC9+8Ig6nScL9Ojrq0/QIAvrq7F+yszAw2FxERGVaD/rUQQnQRQphf+nyoEGKOEMLOsNGIiIiIWkaNSoMX1ybA19EKc8c0fLvFlVxtLDCiqwvWxedApdHpOaHxJV+oxB3zD+B4ThnmTe+Fx4cGQgjDr0L4ZEoE9r00HL187A0+FxERGU5DS9XrAGiFEIEA/gvAG8Byg6UiIiIiakEfbTmLrJIafDQ5HJZmDd9u8U/To31QVKXCttPtq/HkpoRc3P71flTWqbF8VjQmRni02NxKhYCrzdWnjBARUdvS0OKDTkqpATAJwDwp5VwA7oaLRURERNQyYtNLsORgBh7o74foAMdmjTU4yBmedu2n8aRGq8O7m07hyeXH0NXNGpueGtSkLSlEREQNLT6ohRDTAdwPYNOlx/TfWYiIiIioBdWqtHhx7Ql42VvixVuatt3iSkqFwNRIb+xNKUJWcY0eEhpPYWU97ll4GAv3peP+GF+sfCQGbrZcgUBERE3T0OLDTAAxAP5PSpkuhPAHsNRwsYiIiKitKq9V4+1fk7Bw7zkczy5r1f0PPvnjLDKKa/Dh5HBYmZnoZcypfb2gEMCquLa7+iE+sxQT5u3FiZwyfHZXBP51Ww+YmbBPORERNV2D/pWVUp4CMOeKr9MBfGioUERERNQ2SSnx0toEbEnK/+sxcxMFIrzt0MfXHpG+9ujja98qTi2IzyzBov3pmNHPF/27OOltXHdbSwzv6oLVcTl4ZmSwQU+D0DcpJX46lIl/bzoFd1tLrH8sCqEeNsaORURE7UCDig9CiAEA3gbge+keAUBKKQMMF42IiIjamqWHMrElKR+vjO2K23t5Ij6zFPGZpYjLLMV3e85hvk4CALo4d0KkrwOGhDhjbA+3Fjk14Up1ai3mrkmAh60lXh7bVe/jT+vrg22n47D9dAFu6eGm9/ENoValxWs/J2L90fMYFuKMz+/qBVsr7rIlIiL9aOj6wu8BPAsgHoDWcHGIiIiorTp5vhzvbjqNYSHOeHhQABQKgXFh7hgXdrFHda1Ki4ScMsRdKkhsScrHqrhsfHdfJEaFurZo1v/8mYxzRdVYNisancz1s93iSkNDnOFmY4GVR7LaRPEhs7gaj/50FGfyK/DMyCDMGR4EhaJlC0JERNS+NfRf23Ip5e8GTUJERERtVlW9Bk8uPwr7Tqb4dGrPa75wtTRTIjrA8a8TJdRaHUZ8uhtfbE/GyG4uLbb64WhWKRbuPYe7o30wIFB/2y2uZKJUYGpfb8zbkYKc0hp42VsZZJ7mKK6qx9akC/gtMQ8HzxWjk5kSi+7vi2FdXYwdjYiI2qGGbkLcKYT4WAgRI4ToffnDoMmIiIioTZBS4tX1icgqqcGX03rBoVPD+jmYKhV4cnggTp6vwI4zBQZOeVFSbjmeWHYUbjYWeMUA2y2uNDXSCwCw+ki2QedpjJJqFVbEZmHG94cR9d52vLohETmlNXh0SAB+e3oQCw9ERGQwDV35EH3pv5FXPCYBDNdvHCIiImprVh3Jxq8ncvHC6OC/VjU01KRenpi3IwWfb0vB8K6GXf2w5WQenl11AnZWpvju/khYWxi2n4GXvRWGBDtjVVw25owIgomRGk+WVquwNSkfmxPzcCCtGFqdhK+jFWYPDsD4cHeEutu0eM8NIiLqeG5afBBCKADMl1KuboE8RERE1Iacza/EW78mYWCgEx4bGtjo+02VCjw1LAgvrkvAzrMFGN5V/70fpJT4akcqPv0zGb187PDtjD5wsbbQ+zzXMj3KB7OXxmPX2UKMbOG+FlnFNfhw6xlsOZkPrU7Cx8EKjwwOwPgwd3T3YMGBiIha1k2LD1JKnRDiRQAsPhAREdFfalQaPLH8KKwtTPHZXT2hbGKDwkm9PTFvZwq+2JaCYSH6Xf1Qp9Zi7toEbDyRizt6eeK9O8JgYarU2/g3M7yrC1yszbEiNuuGxYdalRa/n8zDmrgcpBRUYkqkNx4a6A+nzuaNnrOiTo2vd6Ri8f4MKBUCDw7ww60RnujhyYIDEREZT0O3XWwTQrwAYBWA6ssPSilLDJKKiIiIWr23fklCWmEVfnooGs7WjX+RfJmpUoEnhgbi5fWJ2HW2UG99B/LL6/DI0jgkni/Hy2O7YvbggBZ/8W2qVGBKpBfm70pDblktPOws/3pOSolj2WVYE5eNjSfyUFWvgY+DFSK87LBgdxoW70/HtL4+mD0kAO62ljeY5SKNVoeVR7Lx2Z/JKK5WYXJvL8wdEwI325ZZ5UFERHQjDS0+3HXpv09c8ZgEEKDfOERERNQWrD+agzXxOZgzPFAvJ0bc0dsLX+1MxefbUzA0xLnZRYIT2WV4+Mc4VNdr8N2MyBbf8nClaX198PXONKyOy8YzI4NRUFGH9cfOY01cNtIKq2FpqsS4MHdMifRClJ8DFAqBtMIqzN+Vhp8OZWLZ4UxM7u2Fx4Z2ga9jp2vOsSe5EP+3+TTOXqhElJ8DfpgZijAv2xb+TomIiK5PSCmNnaFRIiMjZVxcnLFjEBERdVhphVWYOG8fwjxtsfzhfk3ebvFPK2Kz8Mr6RCye2RfDQpq++uGX4+fx4toEOFub4/v7+yLEzVov+ZpjxveHcTa/EmGettiVXAitTiLS1x5TIr0wPtwDnc2v/X5QTmkNvt19DqvisqHR6jAxwgNPDAtEsOvF7ym1oAr/t/kUdp4thI+DFV4Z2xW39HDj9goiIjIaIUS8lDLyqscbUnwQQtx3rcellD/qIVujsPhARERkPHVqLW7/ej8KKuvx25xBel3Sr9LoMOyTXXC2NseGx/s3+gW0Tifx2bZkzNuRiih/B8y/pzccm9AzwRC2JuVj9tJ4uFibY3IfL9zZxwtdnDs3+P6Cijos3JeOnw5lokalxZjurnCxtsDy2CxYmSrx5PBAPDDAD+YmLdfPgoiI6FqaW3yYd8WXFgBGADgqpbxTfxEbhsUHIiIi43ltQyKWHc7CDzP7YmgzVidcz/LDWXh1QyKWPBiFIcHODb5PpdHhudXHsSkhD3dFeuOd23vAzMQ4R1teT2pBJfwcOzXryM3SahUWH8jAD/vTUVWvwfQoHzw7KrhJjSmJiIgMoVnFh2sMZgdgpZTyFn2EawwWH4iIiIzjTH4Fbvl8Lx4e5I/XxocaZI7Lqx9cbMyx/rGGrX6oU2vx+LKj2HGmwGiNJVtadb0GNSptsxp9EhERGcL1ig9NLb1XA/BvXiQiIiJqS5YcyICFqQJPDAs02BxmJgo8PqwLjmWVYW9K0U2vr67XYObiI9h5tgDvTQrDo0O6tPvCAwB0Mjdh4YGIiNqUBhUfhBAbhRC/XvrYBOAsgJ8NG42IiIhai7IaFTYcO4/be3rCzsrMoHNN6eMND1sLfL4tGTdaoVleo8a93x9GbEYJPpvaE3dH+xg0FxERETVdQ4/a/OSKzzUAMqWUOQbIQ0RERK3Q6rhs1Kl1uL+/n8Hnurj6IRCv/3wS+1KLMCjo6t4PRVX1mPF9LNIKqvD13b1xSw83g+ciIiKipmvototxUsrdlz72SylzhBAfGjQZERERtQpancSPBzMR7e+Abu42LTLnlEgvuNta4IttKVetfsgrr8Vd3x5EelEVFt4fycIDERFRG9DQ4sOoazw2Vp9BiIiIqHXafvoCckpr8UALrHq4zNxEiceHBSIusxT7U4v/ejyruAZTFhzEhYp6/PhgNAY34kQMIiIiMp4bFh+EEI8JIRIBhAghEq74SAeQ0DIRiYiIyJiWHMyAh60FRoW6tui8Uy+vfth+sfdDakElpnx7AFX1Gix/OBpR/g4tmoeIiIia7mY9H5YD+B3A+wBevuLxSillicFSERERUauQcqES+1OLMXdMCEyUTT0kq2nMTZR4bGgXvPlLEr7fl45vdqVBqRBY9UgMQtysWzQLERERNc8Nf4uQUpZLKTOklNMBeAMYLqXMBKAQQvCoTSIionZuycEMmJkoMD3KOCdJTI30hpuNBd7dfBqWpkqsns3CAxERUVvU0KM23wLwEoBXLj1kBuAnQ4UiIiIi4yuvVWNd/HncFuEBh06GPV7zeixMlXh9QjdE+ztg9aMx8HfqZJQcRERE1DwNPWpzEoBeAI4CgJQyVwjBtx2IiIjasTVx2ahVa1vkeM0bmRDugQnhHkbNQERERM3T0M2bKnnxnCsJAEIIvu1ARETUjl0+XjPS1x49PG2NHYeIiIjauIYWH1YLIb4FYCeEeBjANgALDReLiIiIjGnX2QJkldTggQF+xo5CRERE7UCDtl1IKT8RQowCUAEgBMCbUso/DZqMiIiIjOaHAxlws7HAmO5uxo5CRERE7UBDez7gUrHhTwAQQiiEEPdIKZcZLBkREREZRVphFfamFOH5UcEwbeHjNYmIiKh9uuFvFEIIGyHEK0KIr4QQo8VFTwI4B2Bqy0QkIiKilvTjgQyYKRWYHm2c4zWJiIio/bnZyoelAEoBHAQwC8CrAASA26WUxw2cjYiIiFpYZZ0aa+NzMCHcHU6dzY0dh4iIiNqJmxUfAqSUYQAghFgIIA+Aj5SyzuDJiIiIqMWtjc9Btcr4x2sSERFR+3KzjZzqy59IKbUAchpaeBBCLBJCFAghTl7n+XuEEAlCiEQhxAEhRETDYxMREZG+6S4dr9nLxw4R3nbGjkNERETtyM2KDxFCiIpLH5UAwi9/LoSouMm9PwC45QbPpwMYcmllxTsA/tvg1ERERKR3u1MKkV5UjQe46oGIiIj07IbbLqSUyqYOLKXcI4Twu8HzB6748hAAr6bORURERM235EAGnK3NMbaHu7GjEBERUTvTWs7PegjA78YOQURE1FGlF1Vj19lC3BPtAzOT1vLrAREREbUXN2s4aXBCiGG4WHwYeINrHgHwCAD4+PDYLyIiIn378WAGTJUCd/N4TSIiIjIAo761IYQIB7AQwG1SyuLrXSel/K+UMlJKGens7NxyAYmIiDqAshoVVh/Jxrgwd7hYWxg7DhEREbVDRis+CCF8AKwHMENKmWysHERERB3d9/vSUa3S4vGhgcaOQkRERO2UwbZdCCFWABgKwEkIkQPgLQCmACClXADgTQCOAL4RQgCARkoZaag8REREdLXyGjV+2J+BsT3cEOJmbew4RERE1E4ZrPggpZx+k+dnAZhlqPmJiIjo5hbtT0dlvQZzRgQZOwoRERG1Y2xnTURE1EGV16qxaH86xnR3RTd3G2PHISIionaMxQciIqIO6of9Gais0+Cp4Vz1QERERIbF4gMREVEHVFmnxvf7zmFkN1f08LQ1dhwiIiJq51h8ICIi6oCWHMhARZ0GT7PXAxEREbUAFh+IiIg6mKp6DRbuS8eIri4I8+KqByIiIjI8Fh+IiIg6mB8PZqCsRs0TLoiIiKjFsPhARETUgVTXa/DdnnMYGuKMCG87Y8chIiKiDoLFByIiog5k6aFMlHLVAxEREbUwFh+IiIg6iBrVxVUPg4Kc0NvH3thxiIiIqANh8YGIiKiDWHYoC8XVKjwzkqseiIiIqGWZGDsAERHRzayNz8HGE7no4WmDSD8H9Paxh62lqbFjGURiTjm+3pmKW3t6YHSoK0yU+nmfoFalxbd70jAw0Al9fB30MiYRERFRQ7H4QERErZaUEt/sSsPHW8/C3dYC+1KLoN2ZBiGAEFdr9PVzQKSfPfr6OcDDztLYcfXiv3vPYUtSPrYk5cPTzhIzYnwxra837KzMmjXu8tgsFFWp2OuBiIiIjILFByIiapV0Ool3N5/Gov3puL2nBz6eEgG1VofjWWU4klGKuMwSrD+ag6WHMgEAnnaWiPSzx209PTC8q6uR0zdNrUqL7acv4K5Ib4zo5oLF+zPwwe9n8Pm2ZEzq5YWZA/wQ7Grd6HHr1Fos2J2GmABHRPlz1QMRERG1PBYfiIhIr6SUWBGbjf/8eRZTI70xZ0QQLEyVjRpDrdXhxbUJ2HDsPGYO8MMb40OhUAiYKhXoH+iE/oFOAACNVocz+ZU4klGCuIxS7E8txq8ncvH5XT1xW09PQ3x7BrXrbAFqVFrc2tMDAwKdMLq7G07nVWDJgQysP5qDFbFZGBjohAf6+2F4VxcoFKJB466IzUJhZT3mTe9l4O+AiIiI6NqElNLYGRolMjJSxsXFGTsGERFdQ3W9Bq9tSMTPx3MR4NQJ54qq4etohfcmhWHApYLBzdSqtHh8WTx2ni3E3DEheHxoFwjRsBfZdWotHlgciyMZpfjmnt4Y092tOd9Oi3ty+VEcTCvG4VdHXNXroaRahRWxWVh6MBP5FXXwdbTC7T094eNgBXdbC7jZWsDd1hKWZn8v9NSptRjy8U74OnbC6tkxLfntEBERUQckhIiXUkZe9TiLD0REpA9n8yvx+LJ4pBdV49mRwXhiWCAOnivGaxsSkVFcgzt6e+L18aFw6HT93gVlNSo8tCQOx7JK8e7tYbg72qfROarqNbh34WGcyq3Ad/dHYkiwc3O+rRZTq9Ki9zt/4o7envi/SWHXvU6t1WFrUj4W789AfGbpVc/bWprC3dbiUkHCElX1Gmw8kYvls6L/WjFCREREZCgsPhARkcGsjc/B6z8norO5Kb6c1vNvL3Lr1Fp8tSMVC3anwdrCBG9MCMWkXp5XrWbIL6/D/YtikV5UjS+m9cTYMPcm5ymvUWPad4eQXlSFJTOjEB3g2OSxWspviXl4fNlRLH84Gv27NKxIUKfWIr+8Dnnldcgrr0Veed2lr//3eXG1CgMCHfHTQ9ENXkFCRERE1FQsPhARkd7VqrR485eTWBOfg34BDvhyei+4WFtc89qz+ZV4eX0CjmWVYWCgE/5vUg/4OnYCAKQXVePehYdRVqPCd/dF6uUd+qKqetz17UFcqKjHslnRiPC2a/aYhvTEsqM4nF6Mw6+OhLKBvRwaok6thalSodcxiYiIiK7nesUH/RweTkREHU5aYRVu/3o/1h7NwVPDA7FsVr/rFh4AIMTNGuse7Y93buuO49llGP3ZHszflYZjWaW4c/4B1Km1WPlIjN62Bjh1NseyWf1g38kU9y2Kxem8Cr2Mawg1Kg22n7mAsT3c9V4ksDBVsvBARERERsfiAxERNdovx89j4rx9KKyqxw8zo/D86JAGvcBVKARmxPhh23NDMCzEBR9uOYNJ3xyAhakSax6NQZiXrV5zutlaYPmsfrA0VWLG94dxrrBKr+Pry44zBahT6zA+vOlbTYiIiIhaMxYfiIiowdRaHV7bkIinVx5HqLsNNs8Z2KSGjm62Flgwow++ndEHk3p5Yt1j/RHg3NkAiQFvByssezgaAHDPwsPILqm54fWl1Sr8cvw8nlt1HP3e2475u9IMkutKmxPy4Gxtjr5+Dgafi4iIiMgYTIwdgIiI2oaqeg0eX3YUe5IL8cjgAMwdEwJTZfNq2GO6u7XIcZhdnDvjxwejMe2/B3HPwsNY82gMXG0ubhHR6SRO5pZj19lC7DpbgOPZZdBJwN7KFK42Fvh46xn09rEzWNPK6noNdpwpwLS+3tweQURERO0Wiw9ERHRTBZV1ePCHIzidV4kPJ4fhrr6NPwLT2EI9bLDkwSjcu/Aw7ll4GE8M64K9KUXYk1yIoioVhADCvezw1PAgDA1xRriXHWrVWkz4ci+eWXUcv80ZBPsbHBPaVNvPFKBeo8P4cA+9j01ERETUWvC0CyIiuqG0wircvygWxVUqfH1PLwzv6mrsSM1y6Fwx7l8Ui3qNDnZWphgS7IyhIc4YHOQMx87mV12fmFOOO+bvx7AQF3w7o4/ej6ucvTQOx7LKcOiVEVBw5QMRERG1cdc77YIrH4iI6LriM0sxa8kRKITAykf6tfrjKhuiX4Ajfn1yIKpVGkR42d10q0OYly1euqUr3t18Gj8dysSMGD+9Zamq12Dn2ULcHeXDwgMRERG1ayw+EBHRNf156gKeXH4UbrYWWDIzCn5OnYwdSW9C3Kwbdf2DA/yxL7UI72w+jb7+DujqZqOXHNtPX4BKo8MEnnJBRERE7RxPuyAioqssO5yJ2Uvj0NXNGuse69+uCg9NoVAIfDIlAraWpnhy+THUqrR6GXdTQh7cbCzQ28deL+MRERERtVYsPhAR0V+klPj0j7N4bcNJDAl2xopH+sHpGn0QOiKnzub4bGpPpBVW4d+bTjV7vMo6NXafLcS4MHduuSAiIqJ2j8UHIiICAKi1Osxdm4B5O1Ixra83vrsvElZm3J13pYFBTpg9uAtWxGZhc0Jes8badvoCVFodxnPLBREREXUALD4QERF0OonHforH2vgcPDMyCO/fEQYTJf+JuJbnRwcjwtsOL69PQHZJTZPH2ZyQBw9bC/RqB008iYiIiG6Gv1kSEREW7juHbacL8OaEUDwzMljvx0m2J6ZKBeZN6wVI4OmVx6DR6ho9RnmtGnuSi7jlgoiIiDoMFh+IiDq4pNxyfLz1LMZ0d8XMAX7GjtMm+Dha4d1JPXA0qwyfb0tp9P3bTnHLBREREXUsLD4QEXVgdWotnl55HPZWZvjgjnCueGiE23p6YkofL3y9KxUH0ooade9viXnwtLNET265ICIiog6CxQc9kVIiPrOkSctviYiM5f3fTiO1oAqfTo2AfSczY8dpc/51W3f4O3XCs6uOI7estkH3lNeqsSelEOPD3VnsISIiog6DxQc92Zp0AZPnH8SquGxjRyEiapCdZwuw5GAmHhzgj0FBzsaO0yZZmZlg3vReqKjVYNR/dmPh3nM3LUL/eeoC1FqJ8WHcckFEREQdB4sPeqDW6vDhljMAgF+P5xo5DRHRzRVV1WPumgSEuFrjxVtCjB2nTevuYYutzwxGX38HvLv5NCbM24f4zJLrXr85IRde9pYI97JtwZRERERExsXigx4sP5yF9KJqRPk5IDajBPnldcaORER0XVJKvLwuARW1anw+rScsTJXGjtTm+ThaYfEDfbHg3t4or1Vj8vyDeHldAkqrVX+7rrxGjb0pRdxyQURERB0Oiw/NVFmnxhfbU9AvwAHvTw6DlMDmxDxjxyKiDuB0XgVWH8mGStO4XjPLY7Ow7XQBXrwlBN3cbQyUruMRQuCWHu7Y9twQzB4cgLXxORj+6S6sPpINnU4CALaeyodGJzEhzMPIaYmIiIhaFosPzbRgdxpKqlV4dVw3dHHujO4eNth4glsviMiwquo1mLUkDi+uS8Atn+/BzjMFDbovrbAK72w6hYGBTnhwgL+BU3ZMncxN8Mq4btg8ZxACXTrjxXUJmPLtQZzOq8DmhDz4OFihhyeLPkRERNSxsPjQDHnltVi4Nx23Rngg3OvicWkTIzxwPLsM2SU1Rk5HRO3ZR1vOILe8Fq+M7QoAmPnDEdy/KBapBZXXvUel0eGZlcdhYarEp1MjoFBw2b8hhbhZY/XsGHx8ZzjSi6oxYd4+7E0pxLgwbrkgIiKijofFh2b49I9kSAnMHfO/Zm2Xu5dvTODqByIyjNj0Evx4MBP3x/hh9pAu2PLMYLw+vhuOZpVizOd78favSSivUV913+fbkpF4vhwf3BEGVxsLIyTveIQQmBLpjR3PD8Fdfb1haarEHb09jR2LiIiIqMWx+NBEp3IrsO5oDu7v7wtvB6u/Hvd2sEJvHztsPMG+D0Skf3VqLV5alwAve8u/Cp9mJgrMGhSAXS8MxV19vfHjwQwM/WQnlh7M+OvYx8PnijF/dxqmRnrhlh484rGl2VmZ4b1JYTj5rzEIdrU2dhwiIiKiFsfiQxO9//tp2FiY4slhQVc9NzHCA6fzKpBaUGWEZETUnn2+LQXpRdX44I5wdDI3+dtzjp3N8d6kMGx6ahBC3Kzxxi9JGP/lPvyRlI/nVp+Aj4MV3prY3UjJCQC3WxAREVGHxeJDE+xJLsTelCI8NTwQtlamVz0/PswdQgCbuPWCiPQoMacc3+09h6mRXhgY5HTd60I9bLDi4X5YcG9v1Kg1eGRpPPIr6vD5XT2vKlgQEREREbUE/hbaSFqdxHu/nYbn8wulAAAgAElEQVS3gyVmxPhe8xoXGwv083fExhO5eHpEEN/pIqJmU2l0mLv2BBw7meG18aE3vf7ysY9DQ1zw06FMOHU2Ry8f+xZISkRERER0Na58aKT1R3NwJr8Sc8d0hbmJ8rrXTYzwQFphNU7nXb/zPBFRQy3YnYYz+ZV49/YesLW8esXV9ViYKjFrUABu78Umh0RERERkPCw+NEKtSotP/0hGhJctJobfuGHbLT3coFQInnpBRM2WcqES83akYEK4O0Z3dzN2HCIiIiKiRmPxoREW7U9HfkUdXh3X7aZbKRw6mWFgoBM2nsiFlLKFEhJRe6PVScxdm4DO5ib4161sFklEREREbROLDw1UVFWP+bvSMLKbK6IDHBt0z8QID+SU1uJ4dpmB0xFRe7V4fzqOZ5fh7Vu7w7GzubHjEBERERE1CYsPDfTl9hTUqrV4eWzXBt8zursrzJQKbDyRZ8BkRNReZRZX45M/zmJEVxfcGuFh7DhERERERE3G4kMDnCuswvLDWZjW1xuBLp0bfJ+NhSmGhjhjU0IutDpuvSCihpNS4uV1iTBVKPDupB48NYeIiIiI2jQWHxrgoy1nYW6iwDMjgxt978QIDxRU1uNIRokBkhFRe7XySDYOnivGK+O6wd3W0thxiIiIiIiahcWHm9ifWoQtSfmYPaQLnK0bv996RDcXWJoqsfEET70goobJL6/De5tPIybAEdOjvI0dh4iIiIio2Vh8uAGVRoc3fzkJHwcrPDI4oEljWJmZYGSoK34/mQ+NVqfnhER0JSkl9qUUIa6NrzRatD8ddRotPpgcxu0WRERERNQusPhwA9/vS0daYTXevjUUFqbKJo8zMdwdJdUqHEgr1mM6IrpMrdXh52PnMe7Lfbj3+8OYufgIymvUxo7VJDqdxOaEPAwOcoavYydjxyEiIiIi0guDFR+EEIuEEAVCiJPXeV4IIb4UQqQKIRKEEL0NlaUpzpfV4svtKRgV6orhXV2bNdaQEGdYW5hw6wWRnlXXa/D9vnQM/XgXnll1HBqtDnPHhKCyXoPFB9KNHa9JjmWX4nxZLSZEuBs7ChERERGR3pgYcOwfAHwF4MfrPD8WQNClj2gA8y/9t1V4d9MpSEi8OSG02WOZmygxprsbtiTl491JPWBu0vRVFEQEFFbW44cD6fjpUBbKa9WI8nfAv2/rjmEhLlAoBBJyyrBoXzoeHOgPGwtTY8dtlI0n8mBuosDIbs0rehIRERERtSYGW/kgpdwD4EYbr28D8KO86BAAOyHETd/qq6rX6Cvide1OLsTvJ/Px5LBAeDtY6WXMCeHuqKzTYE9ykV7GI+qIzhVW4ZX1iRjw4Q58sysNMQGOWP94f6yeHYMR3VyhUFzsj/DU8CBU1Gnw44EM4wZuJK1OYnNiHoaFuMC6jRVNiIiIiIhuxJArH27GE0D2FV/nXHos70Y3ZRRXI7WgCoEunQ0Sql6jxVu/nESAUyc83MQmk9cyINAJ9lam2HgiF6NC+Y4mUWMtPZSJN385CVOlAnf28cKsgf4IcL72z4EenrYY2c0FC/el44EB/uhsbswfdQ0Xm16Cwsp6TIzwMHYUIiIiIiK9ahMNJ4UQjwgh4oQQcUJKzFlxDPUarUHm+u/uc8gorsHbt3bX6/YIU6UCY8Pc8eepC6hRGX71BlF7cqGiDh/8dhr9uzhi/0vD8d6ksOsWHi57angQymrUWHows4VSNt/GhFxYmSkxvKuLsaMQEREREemVMYsP5wFceYC916XHriKl/K+UMlJKGent2Amn8irw8Zazeg+UXVKDr3amYlyYGwYHO+t9/InhHqhVa7HjTIHexyZqKSqNDl9sS0FWcU2LzfnB72eg1km8NykMztbmDbonwtsOQ0Oc8d3ec22i4KfW6rDlZD5GdnOFpRn7whARERFR+2LM4sOvAO67dOpFPwDlUsobbrkAABsLU9wX44uF+9KxJ7lQr4H+tfEUlAqBN/TQZPJaovwd4GJtzlMvqE3796YkfLYtGR9uPdMi88VllGDDsfN4ZFBAo4+efGp4EEqqVVh2KMtA6fTnQFoxSqpVmBDOUy6IiIiIqP0x5FGbKwAcBBAihMgRQjwkhHhUCPHopUt+A3AOQCqA7wA83tCxXx3XDcGunfHc6hMoqqrXS97tpy9g2+kLmDMiCO62lnoZ85+UCoHx4e7YebYQ2SUt964xkb6sjc/BT4ey4GZjga0n85FfXmfQ+bQ6ibc3JsHNxgKPD+vS6Pv7+NpjUJATvt1zDrUqw2zV0pdNJ3JhbWGCISH6X3VFRERERGRshjztYrqU0l1KaSql9JJSfi+lXCClXHDpeSmlfEJK2UVKGSaljGvo2BamSnwxrRcq6tR4aW0CpJTNylqn1uLtjUkIdOmMBwf4N2usm7k7ygfmSgXGfbEXv+phBUSNSoPSapUekhHd2Mnz5XhtQyJiAhyx/OFoaKXE8ljDrihYHZeNk+cr8Or4brAya1rTyDkjglBUVY8VBs7aHPUaLbYk5WN0qBuP4iUiIiKidqlNNJy8lm7uNnh1bFdsP1OApYea11Dum11pyC6pxb9v6w4zE8P+kQS5WuO3pwchyLUz5qw4hhfWnGjS8aFancSyw5kY9OFO3Pb1/mYXYIhupLRahUd/iodDJzPMu7sXApw7Y1iIC5YfzoJKozPInOU1any89Syi/B0wsRlbEfr6OSAmwBELdqehTt06Vz/sSS5CZZ0GEyO45YKIiIiI2qc2W3wAgPv7+2FYiDPe3XwaZ/MrmzRGRlE1FuxOw60RHujfxUnPCa/N28EKq2fHYM6IIKw/moMJX+7FieyyBt9/IK0I47/ci9c2nISZiQJZJTU4e6Fp3z/RzWh1Ek+vOo6CinrMv7cPnDpfbPg4I8YXRVX1+P3kTVu1NMln25JRVqPC2xO7QwjRrLHmjAhCQWU9Vsdl3/xiI9iUkAt7K1MMCGyZn0FERERERC2tTRcfhBD4eEoEbCxMMWfFsUa/qynlxf3kZkoFXhvfzUApr81EqcBzo4Kx8pEYqDQ6TJ5/APN3pUGnu/4KhqziGsxeGoe7vzuMyjoNvr67NzY8PgAAsPusfptvEl32+bZk7EkuxNu3dkdPb7u/Hh8S5AxfRyuDHGV5Jr8CSw9l4p5oX4R62DR7vH4BDojyc8D8XWkGO6a3qWpVWvx56gJu6eEOU2Wb/pFMRERERHRdbf43XafO5vhkSjjOXqjEB783rvv+H6cuYNfZQjwzMgiuNhYGSnhjUf4O+P3pwRjd3RUfbjmDe78/fFUTv8o6NT74/QxG/mc39qYU4YXRwdj+/BCMD3eHm60FurpZY08Kiw+kf3+euoB5O1IxNdIL06O8//acQiEwo58v4jJLcfJ8ud7mlFLi7V+TYG1hgudGBetlTCEE5owIQl55HdbG5+hlTH3ZebYANSpts7aWEBERERG1dm2++AAAQ0Nc8OAAf/xwIAM7zly44bUqjQ5/JOXj0aXxeHL5UXR1s8YD/f1aJuh12FqZ4uu7e+OjyeE4llWGsV/swR9J+dDqJFYdycKwT3Zjwe40TIzwwM4XhuLJ4UGwMP1fU7rBwc44kl6KGlXje0cQXU96UTWeW3UcYZ62+PdtPa659WFKH29Ymir1uvrht8R8HDpXgudHh8C+k5nexh0Q6IjePnb4Zmea3vtUaHUSvxw/j/IadaPv3XgiF06dzREd4KjXTERERERErUm7KD4AwEtjQ9DN3QYvrElAQeXfVw5IKZGQU4a3fjmJ6Pe24ZGl8YjLLMF9MX5Y9EBfmLSCpc5CCEzt641NcwbC094SjyyNx5CPd+KldYnwdbTCL08MwKdTI665QmNIsDNUWh0OnSs2QnJqj2pUGjy6NB4mSoH59/b+W7HrSrZWpri9lyd+Pn4eZTXNP3WlVqXF/20+hW7uNrg7yqfZ413p8uqH82W12HBMv6sffj52Hk+vPI4nlh+F9gZbp/6pql6DHWcKMD7MDUpF8/paEBERERG1ZsZ/1a0n5iZKfDmtJ6rrNXh+9QnodBJ55bX4ZlcqRn22B7d+tR8rjmRjQKATFj/QF4deGYE3JoTCw87S2NH/potzZ6x/bABmDw6ApakSX0zribWPxiDiir32/xTpZw9LUyX7PpBeSCnx0rpEpBRU4svpveBlb3XD6++L8UW9RqeXZo7zd6cht7wO/7q1u0FejA8JdkaEly2+2pkKtVY/qx9UGh0+354MeytT7EstwhfbUxp877ZTF1Cv0WFihIdeshARERERtVYmxg6gT0Gu1nhjQihe//kkxn25F2cvVEJKoK+fPT64Iwxjw9xha2lq7Jg3ZWaiwCvjuuGVcQ1rgmluokRMF0fsTmbxgZpv0f4MbDyRi7ljQjAoyPmm13dzt0GUnwOWHsrEQwMDmlw0yC6p+evkmSh/hyaNcTOXVz88tCQOvxzPxZ19vJo95qq4bGSX1GLxA32xOTEP83akoI+vPYYE3/zPblNCLtxtLdDbx77ZOYiIiIiIWrN2s/LhsnuifXBbTw/Ua3R4ekQQds8dijWP9se0KJ82UXhoqsFBTsgorkFmcbWxo1AbdvhcMd777TRGh7ri8aFdGnzfff19kV1Si93JBU2e+93Np2CiEHi1gUW3phre1QXdPWzw9c5UaJq5+qFWpcW87Sno62ePoSHOeOe2HghxtcYzK48ht6z2hveW16ixO7kQ48PcoeCWCyIiIiJq59pd8UEIgS+m9cLOF4bimZHB8HXsZOxILWJIiAsAYA9XP1ATlVar8OSKY/B1sMKnUyOu2WDyesZ0d4OLtTmWHGha48m9KYXYmnQBTwwLhJutYU+eubz6Ib2oGhsTcps11tJDGSiorMfcMV0hhIClmRLf3NMbaq3Ek8uP3rCx5dZT+VBrJbdcEBEREVGH0O6KDx2Vn6MVvB0ssTu5yNhRqI1689cklNWoMO/uXrC2aNwqIVOlAvdE+2J3ciHSixq3+kat1eFfG0/B19EKDw30b9S9TTWqmyu6e9jg/d/OoKS6aY0yK+vU+GZXGgYHO/9tm0iAc2d8ODkcR7PKbnj878YTufBxsEK4l22T5iciIiIiaktYfGgnhBAYEuyMg2lFej9GkNq/zQl52HgiF3OGB6G7R9NeDE+P9oapUjTq2E21VoeX1iUgtaAKb4wPve6pGvqmUAh8MiUCZTVqvLg2AVI2/ISKyxbuTUdZjRpzR4dc9dz4cHc80N8Pi/an4/fEvKueL66qx4G0YkwId2/UChMiIiIioraKxYd2ZHCQM6pVWsRnlho7CrUhhZX1eP3nRER42eKxRvR5+CcXawuM7eGONfHZqK7X3PT6GpUGD/8Yh/VHz+PZkcEYGera5Lmbopu7DV4a2xXbTl/A8tisRt1bUq3C9/vSMbaHG8Kus3Lh1XHd0NPbDnPXJly1GuT3k/nQ6iQmhHPLBRERERF1DCw+tCP9A51gohA89YIaTEqJ1zYkolqlxadTI2CibN6PhPv7+6KyToOfj5+/4XUl1Src/d1h7EkuxHuTwvD0yKBmzdtUM/v7YVCQE97ZdAqpBZUNvm/B7jRUqzR4blTwda8xM1Hg63t6w0Qp8NhP8ahTa/96blNCLro4d0I3d+tm5SciIiIiaitYfGhHOpuboI+vPZtOUoNtOHYef5y6gLmjQxDo0vwXwr197BHqboOlBzOvu5Uhp7QGdy44gFN5FZh/bx/cHe3T7HmbSqEQ+HRKBKzMTDBnxXHUa7Q3vedCRR2WHMjApF6eCHK98Z+Zp50lPrurJ87kV+LNX07+df/h9BJMCPfglgsiIiIi6jBYfGhnhoQ441ReBQoq64wdhVq5vPJavPVrEiJ97fGgnho9CiFwf39fnMmvRGx6yVXPn8mvwOT5B1BUWY9ls6IxprubXuZtDhcbC3w4ORyn8irw6R/JN71+3o4U6KTEsyOvv+rhSsNCXPDksECsjsvB6rhs/JaYBymBiRHuzY1ORERERNRmsPjQzgwOcgYA7OWpF3QDUkq8tC4RGq3EJ1MioFTo7x34WyM8YWtpih//0Xjy8LliTFlwEAICax7tj75+DtcZoeWNCnXFPdE++O+ec9iXcv2/O1nFNVgZm427+nrD28GqweM/OyoY/bs44o2fT2LJgQx0dbPWy0oTIiIiIqK2gsWHdibU3QZOnc3Z94FuaOWRbOxJLsQr47rCz6mTXse2NFPirr7e2JKUj/zyiytwtpzMw4xFsXCxNse6x/sjxK31vfB+fXwoujh3wvNrjqP0Osdvfr4tGUqFwFPDG9ejQqkQ+GJaL9hamiKjuAYTI9hokoiIiIg6FhYf2hmFQmBwkBP2phRCq2v88YHU/mWX1ODdTacwINAR90b7GmSOe6N9oZMSyw9n4qdDmXh82VF097DB2kf7w9PO0iBzNpelmRJfTOuFkmoVXl5/9fGbyRcqseH4eTzQ3w+uNhaNHt/Z2hzf3NMbUf4OmNzbS1+xiYiIiIjaBBYf2qEhIc4orVHj5PlyY0chA9uUkIvXNiQiIaesQdfrdBJz156AEAIf3RkBhR63W1zJx9EKw0Nc8O2ec3j955MYFuKC5bP6wb6TmUHm05cenrZ4cUxXbE26gJVHsv/23H/+SEYnMxM8OqTpx5FG+jlg9ewYuNk2vnhBRERERNSWsfjQDg0MdIIQ4NaLdm53ciGeXnkcyw5n4dav9uP2r/djw7GcG57YsORgBg6dK8GbE0INvgJh5gB/1Gt0mNLHC9/O6ANLM6VB59OXhwb6Y2CgE/698RTSCqsAAAk5ZdiSlI9Zg/xbfQGFiIiIiKg1YvGhHXLsbI4wT1seudmOncqtwOM/xSPIpTMOvjIcb08MRUWtGs+uOoH+7+/Ax1vP4HxZ7d/uOVdYhQ+3nMGwEGdMiTT8sv+BQU7Y//JwfHRnOEyUbedHjUIh8OnUCJibKvDMyuNQaXT45I9k2FuZ4iE9nQpCRERERNTRtJ1XBNQoQ4KdcSy7DOW1amNHIT3LK6/Fgz8cgbWFKRbP7At3W0s8MMAf254bgqUPRaG3rz3m70rDoA93YPbSOOxPLYJGq8MLa07A3ESJDyaHQwjDbLf4J087yxabS59cLx2/mXi+HA//GIc9yYV4bGgXWFuYGjsaEREREVGbZGLsAGQYg4OdMW9HKg6kFmFsmLux45CeVNapMXPxEVTVa7Dm0Ri42/5v64RCITAoyBmDgpyRU1qDZYezsOpINrYmXYBTZ3MUVdXji2k9m9QssSMa090N06N8sCI2C6425rgvxs/YkYiIiIiI2iwWH9qpXt52sLYwwe7kQhYf2gm1VofHlx1FakEVFj3QF93cba57rZe9FV66pSueHhGEzQl5WHY4EyO7ueBWHvHYKG9M6IaiqnpM7u0FC9O20bOCiIiIiKg1YvGhnTJRKjCgixP2JBdCStkml77T/0gp8er6ROxNKcJHd4ZjcLBzg+6zMFVich8vTO7Dox2bwsrMBN/dF2nsGEREREREbR57PrRjQ0KckVteh9SCKmNHoWaatyMVa+JzMGd4IKZGehs7DhERERERUaOw+NCOXX53nEdutm3rj+bgP38m445ennh2VLCx4xARERERETUaiw/tmKedJQJdOrP40IYdSC3CS+sSEBPg2KKnVBAREREREekTiw/t3OAgZxxOL0GtSmvsKNRIyRcqMfunePg5dsKCGX1gZsK/rkRERERE1Dbx1Uw7NyTEGSqNDofTi40dhRohvagaMxcfgYWpEotn9oWtpamxIxERERERETUZT7to56L9HWBuosDu5EIMDXExdhy6icSccizYk4bfE/NgaarEqtkx8LK3MnYsIiIiIiKiZmHxoZ2zMFUiOsARe9j3odWSUmJvShEW7E7DgbRiWJub4JHBXTBzgB9cbSyMHY+IiIiIiKjZWHzoAAYHOeHdzaeRU1rDd9FbEY1Wh82Jefh29zmcyquAq405XhnbFXdH+8DagtssiIiIiIio/WDxoQMYGuKMdzefxp7kItwd7WPsOB1ejUqD1UeysXBfOnJKa9HFuRM+ujMct/X0gLmJ0tjxiIiIiIiI9I7Fhw6gi3NneNpZYndyAYsPRnY6rwL3LDyMkmoV+vja462J3TGiqwsUCh6hSURERERE7ReLDx2AEAIxXRyx/fQF6HSSL3SN6P3fz0AnJdY+GoNIPwdjxyEiIiIiImoRPGqzg4jyd0BpjRqphVXGjtJhxaaXYE9yIR4b0oWFByIiIiIi6lBYfOggov0vvtg9nF5i5CQdk5QSn2w9C2drc9wX42fsOERERERERC2KxYcOwsfBCm42Fohl8cEo9qQUITajBE8ND4SlGZtKEhERERFRx8LiQwchhECUvwNi04shpTR2nA7l8qoHL3tLTOvLhp9ERERERNTxsPjQgUT5O+BCRT0yi2uMHaVD2ZqUj8Tz5Xh6RBDMTPhXjoiIiIiIOh6+EupALvd94NaLlqPVSXz6RzICnDthUi9PY8chIiIiIiIyChYfOpBAl85w6GTGppMt6NcT55FSUIXnRgXDRMm/bkRERERE1DHx1VAHIoRAlJ8DYjOKjR2lQ1BrdfjszxR0c7fBuB7uxo5DRERERERkNCw+dDBR/g7ILqlFblmtsaO0e2vicpBVUoMXRgdDoRDGjkNERERERGQ0LD50MFHs+9Ai6tRafLk9Bb197DC8q4ux4xARERERERkViw8dTDd3G1ibm7Dvg4H9dCgT+RV1eGFMCITgqgciIiIiIurYWHzoYJQKgUg/e8Sms++DoVTXazB/VxoGBDqifxcnY8chIiIiIiIyOhYfOqDoAEekFVajqKre2FHapcX701FcrcILo0OMHYWIiIiIiKhVYPGhA7rc9+EIt17oXXmNGt/uOYeR3VzQy8fe2HGIiIiIiIhaBRYfOqAeHrawNFWy74MB/HdvGirrNHieqx6IiIiIiIj+wuJDB2RmokBvXzsWH/SssLIei/ZlYGKEB7q52xg7DhERERERUavB4kMHFeXniDP5FSivURs7Srvxza5UqLQ6PDsyyNhRiIiIiIiIWhWDFh+EELcIIc4KIVKFEC9f43kfIcROIcQxIUSCEGKcIfPQ/0QHOEBKIC6Tqx/04Y+kfCw7nIXJvT0R4NzZ2HGIiIiIiIhaFYMVH4QQSgBfAxgLIBTAdCFE6D8uex3AaillLwDTAHxjqDz0dz297WCmVCCWWy+apU6txVu/nMQjS+MR7NoZL4xhrwciIiIiIqJ/MjHg2FEAUqWU5wBACLESwG0ATl1xjQRweXO8LYBcA+ahK1iYKhHhbYtDLD40WVphFZ5cfgyn8yrw0EB/vHhLCMxNlMaORURERERE1OoYsvjgCSD7iq9zAET/45q3AfwhhHgKQCcAIw2Yh/4hyt8BC3afQ3W9Bp3MDfm/QvsipcTa+By8+UsSLM2UWPRAJIZ3dTV2LCIiIiIiolbL2A0npwP4QUrpBWAcgKVCiKsyCSEeEULECSHiCgsLWzxkexXl7witTuJoVqmxo7QZVfUaPLvqOOauTUCEty1+mzOIhQciIiIiIqKbMGTx4TwA7yu+9rr02JUeArAaAKSUBwFYAHD650BSyv9KKSOllJHOzs4Gitvx9PG1h1Ih2PehgRJyyjD+y7349UQunh8VjGWz+sHN1sLYsYiIiIiIiFo9QxYfjgAIEkL4CyHMcLGh5K//uCYLwAgAEEJ0w8XiA5c2tJDO5ibo4WGDwyw+3JBOJ7Fw7zlMnn8Aao0Oq2bH4KkRQVAqhLGjERERERERtQkG2+gvpdQIIZ4EsBWAEsAiKWWSEOLfAOKklL8CeB7Ad0KIZ3Gx+eQDUkppqEx0tSh/Byw5mIk6tRYWpmyWeCUpJQ6mFeObXWnYl1qE0aGu+OjOcNhZmRk7GhERERERUZti0C6DUsrfAPz2j8fevOLzUwAGGDID3ViUvyO+25uOE9lliA5wNHacVqGyTo31R89j6aFMpBZUwc7KFO/c1h339vOFEFztQERERERE1Fg84qCD6+tnDyGA2PSSDl98OJtfiR8PZmDDsfOoUWkR4WWLT6ZEYEK4O1eFEBERERERNQOLDx2cnZUZQlytEZvRMfs+qDQ6bE3Kx9KDmYjNKIGZiQK3RnhgRj9fRHjbGTseERERERFRu8DiAyHa3wFr4nOg1upgqjT26astZ0VsFv7zZzIKK+vh42CFV8d1xZQ+3rDvxJ4ORERERERE+sTiAyHK3xFLDmYiKbcCPTvIu/2ZxdV4/eeT6Olth48mh2NIsDMU/9/enUdJVd0JHP/+oNllsQU1AtJshmAUxBVBER0Tk2jW0YmTUaOJjBmMmUyMy8xJciaTzGSSMUclRMdEE8fE3SQaY3ALibvgBgiKbAZQNgVlUWS780eV2IKN3fBeve7q7+ecOlS9eu/e360ftfSv7rvlr1dIkiRJUi5az9fcatCh/XcH4PH5rxYcSeVM+NNcatoEV3xhBGOH7GnhQZIkSZJyZPFB7Nm1IwN6dmHKgtax7sOCV9bx26df4rQj+rFnt45FhyNJkiRJVc/igwA4fEAtU15cyeYtqehQcjfh/jm0axv845iBRYciSZIkSa2CxQcBcFj/Wtas38TspWuKDiVX81as5XfPvMTpI+vo1bVD0eFIkiRJUqtg8UFAadFJgCkLqnvdh8vvn0OHmraMO3pA0aFIkiRJUqth8UEA9O7Rid49OvF4Fa/7MHf5Gu6Y9jJnHFlHz92c9SBJkiRJlWLxQVsd3r+WKQtWklJ1rvtw2f1z6dzOWQ+SJEmSVGkWH7TV4QNqeXXdBuatWFd0KJl7Ydka7pxemvVQ26V90eFIkiRJUqti8UFbvb3uw+NVuO7DZffNoUv7Gs4+ylkPkiRJklRpFh+0Vd0endm3tjPXPLSA9Rs3Fx1OZp5fupo/zFjCmaPq2N1ZD5IkSZJUcRYftFVE8P3PfJh5K9bxo3rdUvsAABBQSURBVLtnFx1OZi69dw5dO9Tw5dHOepAkSZKkIlh80LscNbgXpx3Rj2seXsBj81v+6RczX36dSTOXctbo/nTv3K7ocCRJkiSpVbL4oO1c/PEh9KvtzPm3TGPtW5uKDmeXXHbfHLp2rOGs0f2LDkWSJEmSWi2LD9pO5/Y1XHLKMF5+7U2+d+esosPZac++9Dr3zFrGl0cPoHsnZz1IkiRJUlEsPug9HdyvlnFHD+TGqYv40/PLig5np1x63wt079SOM0fXFR2KJEmSJLVqFh/UoK8fP5ghe3flwttmsGrdhqLDaZJpi17jvueWc/ZR/enW0VkPkiRJklQkiw9qUIeatlxyyjBee2MD37r92aLDaZJL73uBHp3bccaRdUWHIkmSJEmtnsUH7dD++3Tna8cN5s7pS/j9tJebfPyWLSmHqHbs6YWrmDx7BeOOHkBXZz1IkiRJUuEsPuh9nTNmIMP79uBbtz/L8tXrG3XMglfW8fWbnmHItycx6dklOUf4bpfdP4faLu05Y2RdRfuVJEmSJL03iw96XzVt23DJKcNYv3EzF942nZQans2w8NU3+OYt0/ibH/+FPz67hL26deD8W6Yzb8XaisQ6Y/Hr/Hn2Cr40uj9dOtRUpE9JkiRJ0o5ZfFCjDOy1GxeeMITJs1dw09RF292/eNUbXPyb6Rx7yZ+5Y9rLfPHIOh64YCw3jRtJ+5o2nHPdk6x7a1PucU6cPJduHWs4fWS/3PuSJEmSJDWOXw2r0c4YWcc9M5fxH3fOYtSgnvSt7czS19czcfJcbpy6kCD4wuH78k9jB7FXt45bj5tw6kGcdvXjXHjbdCacehARkUt8Lyxbw6SZSznvuMGu9SBJkiRJzYjFBzVamzbBj04+kBMufZBv3DyNoft04/opC0kpccohfRk/dhD79Oi03XGjBvXk/I9+kB9Oms2IfXfnrNH9c4lv4uS5dG7fljP9hQtJkiRJalYsPqhJ+uzemW+fNJQLbp3OkwtXcfLBfRg/dhB9azvv8LivjBnI0wtf4z/veo4D+nTn0LraTON68ZV1/H7ay5x91AB279I+07YlSZIkSbvG4oOa7OSD+7Bbhxr236cb/fbo0qhjIoJLThnGJyc8xPhfP8Wd541mz64d3//ARrriz/OoaduGLx2Vz6wKSZIkSdLOc8FJNVlE8PEDPtDowsPbunVsx5WnHczq9Rs59/qn2bh5SybxvPTam/zm6cWcemjfTAsakiRJkqRsWHxQRQ3Zuxs/+OyBTFmwkh9Oej6TNq/6yzxSgnFjBmbSniRJkiQpWxYfVHGfPqg3Z4zsx88eXMAfpi/ZpbaWr1nPDVMX8bkRfej9HotdSpIkSZKKZ/FBhfi3TwxlxL49uODWacxdvman27n6wQVs2ryFrxzjrAdJkiRJaq4sPqgQ7WvaMPELI+jUvi3n/Oop1r61qcltrFq3gese+ysnDduHup5NW39CkiRJklQ5Fh9UmA9078Tlpx7E/BVrueDWaWzekpp0/C8eeZE3Nmxm/NhBOUUoSZIkScqCxQcV6siBPbnoY0O4a8ZSvviLKaxat6FRx61Zv5FfPryAj+6/F/vt1TXnKCVJkiRJu8Ligwo37uiB/NdnD+Dx+Ss5ccJDzFj8+vsec91jf2X1+k2cO3ZwBSKUJEmSJO0Kiw9qFk49bF9uOWckKSU+d+Uj3DR1YYP7vrFhEz9/cAFj9uvFAX26VzBKSZIkSdLOsPigZmNY3x7ced5RHFZXy4W3zeCi26azfuPm7fa7YcoiVq7bwFePda0HSZIkSWoJLD6oWant0p5rzzqM8WMHcuPURZx85aMsXvXG1vvf2rSZqx6Yx+H9azmkrrbASCVJkiRJjWXxQc1O2zbBNz86hKtOO5gXX1nHSRMe4sE5KwC49cnFLFv9Fl891rUeJEmSJKmlsPigZusj++/N7eeOolfXDpx+zRQm3D+HK/8yj+F9ezBq0B5FhydJkiRJaiSLD2rWBvTajd+NH8VJB+7DJfe+wKKVb3Lu2EFERNGhSZIkSZIaqaboAKT307l9DZd9fjiH1u3O7GVrOO5DexYdkiRJkiSpCSw+qEWICE4bWVd0GJIkSZKkneBpF5IkSZIkKVcWHyRJkiRJUq4sPkiSJEmSpFxZfJAkSZIkSbmy+CBJkiRJknJl8UGSJEmSJOXK4oMkSZIkScqVxQdJkiRJkpQriw+SJEmSJClXFh8kSZIkSVKuci0+RMQJETE7IuZGxEUN7HNKRMyKiJkRcX2e8UiSJEmSpMqryavhiGgLTASOBxYDUyPijpTSrHr7DAYuBkallFZFxJ55xSNJkiRJkoqR58yHw4C5KaX5KaUNwI3Ap7bZ52xgYkppFUBKaXmO8UiSJEmSpALkWXzoDSyqd3txeVt9+wH7RcTDEfFYRJzwXg1FxLiIeCIinlixYkVO4UqSJEmSpDwUveBkDTAYOAY4FfhZRPTYdqeU0lUppUNSSof06tWrwiFKkiRJkqRdkWfx4SWgb73bfcrb6lsM3JFS2phSWgC8QKkYIUmSJEmSqkSexYepwOCI6B8R7YHPA3dss8/vKM16ICJ6UjoNY36OMUmSJEmSpArLrfiQUtoEnAvcDTwH3JxSmhkR342IT5Z3uxt4NSJmAZOBb6aUXs0rJkmSJEmSVHmRUio6hiaJiDXA7AK67g683gr6LKrfnsArFe4TzGslFJHb1vQYm9fq7Ne8Vme/rSmv0LoeYz87VV+fRfVrXqu339b0Hjs4pdR9u60ppRZ1AZ4oqN+rWkOfBY7VvFZvvxXPbWt6jM1rdfZrXquz39aU11b4GPvZqcr6NK/V2WfB/bb699iif+2iJfl9K+mzyH6LYF6rU2t6jM1rdfZrXquz39aUV2hdj3Fryq15rU7mtTo1q8e4JZ528URK6ZCi41C2zGv1MrfVybxWJ/NancxrdTKv1cm8Vi9zm++vXeTlqqIDUC7Ma/Uyt9XJvFYn81qdzGt1Mq/VybxWr1af2xY380GSJEmSJLUsLXHmgyRJkiRJakEKLz5ERN+ImBwRsyJiZkR8rby9NiLujYg55X93L2+PiLg8IuZGxPSIGFGvrR+W23iuvE8UNa7WLuO8/ndEPFu+/F1RY1LJTuR2SEQ8GhFvRcT527R1QkTMLuf9oiLGo5KM83pNRCyPiGeLGIvekVVeG2pHxcgwrx0jYkpETCu38+9FjUnZvg6X728bEU9HxJ2VHovekfH764sRMSMinomIJ4oYj96RcW57RMStEfF8lP6WHVnEmPJWePEB2AR8I6U0FDgCGB8RQ4GLgPtTSoOB+8u3AT4GDC5fxgFXAETEkcAo4EDgw8ChwJgKjkPvllVePwGMAIYDhwPnR0S3Sg5E22lqblcC5wH/U7+RiGgLTKSU+6HAqeV2VIxM8lr2S+CE3CNWY2SV14baUTGyyutbwLEppWGU3mdPiIgjKjEAvacsX4cBvgY8l2/IaoSs8zo2pTS8tS9c2ExkmdvLgEkppSHAMKr0uVt48SGltCSl9FT5+hpKD3Rv4FPAteXdrgU+Xb7+KeD/UsljQI+I+ACQgI5Ae6AD0A5YVrGB6F0yzOtQ4IGU0qaU0jpgOv5RU6im5jaltDylNBXYuE1ThwFzU0rzU0obgBvLbagAGeaVlNIDlN5gVbCs8rqDdlSADPOaUkpryzfblS8uBlaQLF+HI6IP8Ang5xUIXTuQZV7VvGSV24joDhwNXF3eb0NK6bWKDKLCCi8+1BcRdcBBwOPAXimlJeW7lgJ7la/3BhbVO2wx0Dul9CgwGVhSvtydUqrKilFLsyt5BaZR+iamc0T0BMYCfSsQthqhkbltSEM5V8F2Ma9qprLK6zbtqGC7mtfy1PxngOXAvSkl89oMZPB8vRS4ANiSR3zaORnkNQH3RMSTETEulyC1U3Yxt/2BFcAvyqdK/TwiuuQVa5GaTfEhInYDbgP+OaW0uv59qfSTHDusxEfEIOBDQB9Kf8AcGxFH5RSuGmlX85pSuge4C3gEuAF4FNicT7Rqil3NrZon81qdssrrjtpR5WWR15TS5pTScEqfnw6LiA/nEqwaLYPPxCcCy1NKT+YXpZoqo9fh0SmlEZROWR0fEUdnH6maKoPc1lA6zfyKlNJBwDreOVWjqjSL4kNEtKOUsF+nlH5T3rysPO2e8r/Ly9tf4t3ffPcpb/sM8FhKaW15CuEfgapcqKOlyCivpJS+Xz637XgggBcqEb8a1sTcNqTBnKsYGeVVzUxWeW2gHRUk6+dreYrvZDy1sVAZ5XUU8MmIeJHSKY3HRsSvcgpZjZDV8zWl9PZn4+XAbymdwqoCZZTbxcDiejPPbqVUjKg6hRcfIiIond/yXErpx/XuugM4o3z9DOD2ettPj5IjgNfL01oWAmMioqb8n2AMVbpQR0uQVV7L00H3KLd5IKUFRe+pyCD0nnYitw2ZCgyOiP4R0R74fLkNFSDDvKoZySqvO2hHBcgwr70iokf5eifgeOD57CNWY2SV15TSxSmlPimlOkrvrX9KKf1DDiGrETJ8vnaJiK5vXwc+AvirUgXK8Dm7FFgUER8sbzoOmJVxuM1ClGaCFBhAxGjgQWAG75yX9q+Uzpe5GdgX+CtwSkppZTnJP6FUmX8DODOl9ER55fyfUlqsI1FaLfRfKjoYbZVhXjsCT5WPXw2ck1J6pnIj0bZ2Ird7A08A3cr7rwWGppRWR8THKZ2X2ha4JqX0/YoORltlnNcbgGOAnpQW/v1OSunqCg5HZVnllVLhd7t2Ukp3VWgoqifDvNZRWgytLaUvpG5OKX23ciNRfVm+Dtdr8xjg/JTSiZUah94tw+drT0qzHaA0Tf96PzcVK+PPTsMpLRDbHphP6W+hVZUcTyUUXnyQJEmSJEnVrfDTLiRJkiRJUnWz+CBJkiRJknJl8UGSJEmSJOXK4oMkSZIkScqVxQdJkiRJkpQriw+SJClTEbE5Ip6JiJkRMS0ivhERO/zMERF1EfH3lYpRkiRVlsUHSZKUtTdTSsNTSvsDxwMfA77zPsfUARYfJEmqUpFSKjoGSZJURSJibUppt3q3BwBTgZ5AP+A6oEv57nNTSo9ExGPAh4AFwLXA5cAPgGOADsDElNL/VmwQkiQpUxYfJElSprYtPpS3vQZ8EFgDbEkprY+IwcANKaVDIuIY4PyU0onl/ccBe6aUvhcRHYCHgZNTSgsqOhhJkpSJmqIDkCRJrUo74CcRMRzYDOzXwH4fAQ6MiL8t3+4ODKY0M0KSJLUwFh8kSVKuyqddbAaWU1r7YRkwjNLaU+sbOgz4akrp7ooEKUmScuWCk5IkKTcR0Qu4EvhJKp3r2R1YklLaApwGtC3vugboWu/Qu4GvRES7cjv7RUQXJElSi+TMB0mSlLVOEfEMpVMsNlFaYPLH5ft+CtwWEacDk4B15e3Tgc0RMQ34JXAZpV/AeCoiAlgBfLpSA5AkSdlywUlJkiRJkpQrT7uQJEmSJEm5svggSZIkSZJyZfFBkiRJkiTlyuKDJEmSJEnKlcUHSZIkSZKUK4sPkiRJkiQpVxYfJEmSJElSriw+SJIkSZKkXP0/8gAA9S6mo7MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_bcrp.portfolio_return.plot(title='Monthly Rebalanced Best Constant Rebalanced Portfolio', figsize=(18,8)).set_ylabel('Returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph is smoother compared to the daily rebalanced portfolio, and we actually achieve higher returns than the daily rebalanced one by a marginal amount. The monthly rebalancing most likely reduces market noise and follows the traditional mean reversion method to account for the best possible portfolio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through this notebook, we were able to explore the basic functionalities of MlFinLab's newest Online Portfolio Selection module. Readers were exposed to a basic introduction to the OLPS Benchmarks and will be able to replicate results using the simple methods of the new module.\n",
    "\n",
    "The next notebook will focus on Exponential Gradient, a momentum strategy.\n",
    "\n",
    "If you enjoyed reading this please remember to leave us a star on [GitHub](https://github.com/hudson-and-thames) and become a sponsor on [Patreon](https://www.patreon.com/HudsonThames) to have exclusive access to our Slack channel!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Reference: [Online Portfolio Selection](https://books.google.com/books/about/Online_Portfolio_Selection.html?id=R2fdCgAAQBAJ) by Dr. Bin Li and Dr. Steven Hoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
